Disclaimer: The purpose of the Open Case Studies project is to demonstrate the use of various data science methods, tools, and software in the context of messy, real-world data. A given case study does not cover all aspects of the research process, is not claiming to be the most appropriate way to analyze a given data set, and should not be used in the context of making policy decisions without external consultation from scientific experts.
An article recently published in the Lancet journal evaluated global dietary trends and the relationship of dietary factors with mortality and fertility.
GBD 2017 Diet Collaborators. Health effects of dietary risks in 195 countries, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. The Lancet 393, 1958–1972 (2019).
This article evaluated food consumption patterns in 195 countries for 15 different dietary risk factors that have probable associations with non-communicable disease (NCD). For example, over-consumption of sodium is associated with high blood pressure. These consumption levels were then used to estimate levels of mortality and morbidity due NCD, as well as disability-adjusted life-years (DALYs) attributable to sub-optimal consumption of foods related to these dietary risk factors. The authors found that:
“High intake of sodium …, low intake of whole grains …, and low intake of fruits … were the leading dietary risk factors for deaths and DALYs globally and in many countries.”
This figure from the paper’s supplementary materials shows the ranking of the 15 dietary risk factors based on the estimated number of attributable deaths and illustrates how the top 3 risk factors are often issues for many different regions of the world.
This case study will evaluate the data reported in this article to explore regional, age, and gender specific differences in dietary consumption patterns around the world in 2017.
Our main questions are:
In this case study, we will walk you through importing data from PDF files and CSV files, cleaning data, wrangling data, comparing data, joining data, visualizing data, and comparing two or more groups using well-established and commonly used packages, including stringr, tidyr, dplyr, purrr, and ggplot2. We will especially focus on using packages and functions from the Tidyverse. The tidyverse is a library of packages created by RStudio. While some students may be familiar with previous R programming packages, these packages make data science in R especially efficient.
We will begin by loading the packages that we will need:
library(here)
library(readr)
library(dplyr)
library(skimr)
library(summarytools)
library(pdftools)
library(stringr)
library(magrittr)
library(purrr)
library(tibble)
library(tidyr)
library(ggplot2)
library(ggpubr)
library(forcats)
library(lmerTest)
library(car)
library(ggiraph)
library(ggforce)
library(viridis)
library(cowplot)| Package | Use |
|---|---|
| here | to easily load and save data |
| readr | to import the CSV file data |
| dplyr | to arrange/filter/select/compare specific subsets of the data |
| skimr | to get an overview of data |
| summarytools | to get an overview of data in a different style |
| pdftools | to read a PDF into R |
| stringr | to manipulate the text within the PDF of the data |
| magrittr | to use the %<>% piping operator |
| purrr | to perform functions on all columns of a tibble |
| tibble | to create data objects that we can manipulate with dplyr/stringr/tidyr/purrr |
| tidyr | to separate data within a column into multiple columns |
| ggplot2 | to make visualizations with multiple layers |
| ggpubr | to easily add regression line equations to plots |
| forcats | to change details about factors (categorical variables) |
| lmerTest | to perform linear mixed model testing |
| car | to perform Levene’s Test of Homogeneity of Variances |
| ggiraph | to make plots interactive |
| ggforce | to modify facets in plots |
| viridis | to plot in a color palette that is easily interpreted by colorblind individuals |
| cowplot | to allow plots to be combined |
The first time we use a function, we will use the :: to indicate which package we are using. Unless we have overlapping function names, this is not necessary, but we will include it here to be informative about where the functions we will use come from.
Here is an excerpt from the article itself about the context of the work: 
Many dietary factors have well-established associations with health risk. The authors that generated this data set identified 15 dietary factors that have probable health risk based on literature search.
Here you can see a table of the sources for the health risks associated with the dietary factors. RCT stands for randomized control trials.
In the article the authors found that most of the mortality associated with each factor is related to cardiovascular disease.
There are some important limitations regarding the data from this article to keep in mind. The definition of certain dietary factors varied across some of the collection sources. Intakes of certain healthy foods like vegetables and fruits are likely positively correlated with each other and likely negatively correlated with intakes of unhealthy foods. Much of the data was collected with 24 hour recall surveys which are prone to issues due to inaccuracy of memory recall or other biases such as a tendency for some people to report healthier behaviors. The guidelines in the PDF are not specified by gender even though it is known that there are different dietary requirements for optimal health for certain nutrients by gender. The article discusses some limitations about accounting for overall food consumption when calculating consumption of particular foods:
"To remove the effect of energy intake as a potential confounder and address measurement error in dietary assessment tools, most cohorts have adjusted for total energy intake in their statistical models. This energy adjustment means that diet components are defined as risks in terms of the share of diet and not as absolute levels of exposure. In other words, an increase in intake of foods and macronutrients should be compensated by a decrease in intake of other dietary factors to hold total energy intake constant. Thus, the relative risk of change in each component of diet depends on the other components for which it is substituted. However, the relative risks estimated from meta-analyses of cohort studies do not generally specify the type of substitution.
There are also important nuances to keep in mind regarding some of the dietary factors. For example calcium consumption was calculated based on consumption of dairy products, while calcium can be acquired from other sources including plant-based sources. However in these data, the influence of plant-based consumption of calcium was not accounted for, nor was supplementation through vitamin sources.
We will be using data that we requested from the GBD about dietary intake, as well as the guideline data about optimal consumption amounts for different foods contained within the PDF of the article. We have two CSV files, dietary_risk_exposure_all_ages_2017.csv and dietary_risk_exposure_sep_ages_2017.csv. The first one includes consumption levels at the global level and for different countries for all ages combined.
Looking at the CSV file in excel:
Here you can see that the data contains mean consumption values for both men and women in various countries at the national level in 2017 for various foods that may be problematic for health. The units for the food varies. So for example, the mean column in row that says “Diet low in fiber” indicates the average consumption level per person in that region and of that gender of fiber in grams per day.
The second CSV file has similar data, but consumption levels for different age groups are separated.
The authors of this article obtained the data from a variety of sources including household budget surveys and nutritional surveys regarding 24 hour recall of food consumption and 24 hour urinary sodium analysis. The data was derived from sales data from Euromonitor, data from the United Nations Food and Agriculture Organization (FAO), estimates about national availability of specific nutrients, from the Supply Utilization Accounts(SUA), and the United States Department of Agriculture’s National Nutrition Database.
Note: While gender and sex are not actually binary, the data used in this analysis only contains information for groups of individuals described as male or female.
Let’s import our data into R now so that we can explore the data further.
diet_data <- readr::read_csv(here("docs",
"dietary_risk_exposure_all_ages_2017.csv"))
sep_age_diet_data <- read_csv(here("docs",
"dietary_risk_exposure_sep_ages_2017.csv"))First let’s just get a general sense of our data. We can do that using the glimpse() function of the dplyr package (it is also in the tibble package).
Rows: 5,880
Columns: 11
$ year_id <dbl> 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, …
$ location_name <chr> "Global", "Global", "Global", "Global", "Global", "Glo…
$ rei_id <dbl> 111, 111, 112, 112, 113, 113, 114, 114, 115, 115, 116,…
$ rei_name <chr> "Diet low in fruits", "Diet low in fruits", "Diet low …
$ age_group_name <chr> "All Available Ages", "All Available Ages", "All Avail…
$ sex <chr> "Male", "Female", "Male", "Female", "Male", "Female", …
$ parameter <chr> "continuous", "continuous", "continuous", "continuous"…
$ mean <dbl> 8.703202e+01, 9.953182e+01, 1.975187e+02, 1.832338e+02…
$ upper <dbl> 8.988734e+01, 1.023450e+02, 2.083679e+02, 1.921103e+02…
$ lower <dbl> 8.452347e+01, 9.672140e+01, 1.887154e+02, 1.754019e+02…
$ unit <chr> "g/day", "g/day", "g/day", "g/day", "g/day", "g/day", …Rows: 88,200
Columns: 11
$ year_id <dbl> 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, …
$ location_name <chr> "Global", "Global", "Global", "Global", "Global", "Glo…
$ rei_name <chr> "Diet low in fruits", "Diet low in fruits", "Diet low …
$ age_group_id <dbl> 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 30, 31, 32…
$ age_group_name <chr> "25 to 29", "30 to 34", "35 to 39", "40 to 44", "45 to…
$ sex <chr> "Male", "Male", "Male", "Male", "Male", "Male", "Male"…
$ parameter <chr> "continuous", "continuous", "continuous", "continuous"…
$ mean <dbl> 68.54567, 72.62209, 76.73157, 81.03910, 88.79202, 95.9…
$ upper <dbl> 75.16806, 80.00433, 84.73205, 89.52747, 100.94301, 107…
$ lower <dbl> 63.17081, 66.78018, 71.04691, 73.82734, 80.40971, 86.4…
$ unit <chr> "g/day", "g/day", "g/day", "g/day", "g/day", "g/day", …Here we can tell that the sep_age_diet_data is much larger than the diet_data. The diet_data has only 5,880 rows while the sep_age_diet_data has 88,200 rows!
However, both files appear to have the same column structure with 11 variables each.
The skim() function of the skimr package is also really helpful for getting a general sense of your data.
| Name | diet_data |
| Number of rows | 5880 |
| Number of columns | 11 |
| _______________________ | |
| Column type frequency: | |
| character | 6 |
| numeric | 5 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| location_name | 0 | 1 | 4 | 32 | 0 | 196 | 0 |
| rei_name | 0 | 1 | 16 | 39 | 0 | 15 | 0 |
| age_group_name | 0 | 1 | 18 | 18 | 0 | 1 | 0 |
| sex | 0 | 1 | 4 | 6 | 0 | 2 | 0 |
| parameter | 0 | 1 | 10 | 10 | 0 | 1 | 0 |
| unit | 0 | 1 | 5 | 11 | 0 | 2 | 0 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| year_id | 0 | 1 | 2017.00 | 0.00 | 2017 | 2017.00 | 2017.00 | 2017.00 | 2017.00 | ▁▁▇▁▁ |
| rei_id | 0 | 1 | 133.67 | 53.93 | 111 | 114.00 | 118.00 | 123.00 | 333.00 | ▇▁▁▁▁ |
| mean | 0 | 1 | 34.81 | 60.13 | 0 | 0.45 | 8.38 | 42.66 | 566.69 | ▇▁▁▁▁ |
| upper | 0 | 1 | 37.94 | 65.30 | 0 | 0.49 | 9.22 | 46.54 | 624.23 | ▇▁▁▁▁ |
| lower | 0 | 1 | 31.93 | 55.40 | 0 | 0.42 | 7.52 | 39.21 | 513.22 | ▇▁▁▁▁ |
Notice how there is a column providing the number of missing observations for each variable. It looks like our data is very complete and we do not have any missing data. We also get a sense about the size of our data.
The n_unqiue column shows us the number of unique values for each of our columns.
Here is another method using the package summarytools:
### Data Frame Summary
#### diet_data
**Dimensions:** 5880 x 11
**Duplicates:** 0
+----+-----------------+--------------------------------+----------------------+---------------------+--------+---------+
| No | Variable | Stats / Values | Freqs (% of Valid) | Graph | Valid | Missing |
+====+=================+================================+======================+=====================+========+=========+
| 1 | year_id\ | 1 distinct value | 2017 : 5880 (100.0%) |  | 5880\ | 0\ |
| | [numeric] | | | | (100%) | (0%) |
+----+-----------------+--------------------------------+----------------------+---------------------+--------+---------+
| 2 | location_name\ | 1\. Afghanistan\ | 30 ( 0.5%)\ |  | 5880\ | 0\ |
| | [character] | 2\. Albania\ | 30 ( 0.5%)\ | | (100%) | (0%) |
| | | 3\. Algeria\ | 30 ( 0.5%)\ | | | |
| | | 4\. American Samoa\ | 30 ( 0.5%)\ | | | |
| | | 5\. Andorra\ | 30 ( 0.5%)\ | | | |
| | | 6\. Angola\ | 30 ( 0.5%)\ | | | |
| | | 7\. Antigua and Barbuda\ | 30 ( 0.5%)\ | | | |
| | | 8\. Argentina\ | 30 ( 0.5%)\ | | | |
| | | 9\. Armenia\ | 30 ( 0.5%)\ | | | |
| | | 10\. Australia\ | 30 ( 0.5%)\ | | | |
| | | [ 186 others ] | 5580 (94.9%) | | | |
+----+-----------------+--------------------------------+----------------------+---------------------+--------+---------+
| 3 | rei_id\ | Mean (sd) : 133.7 (53.9)\ | 15 distinct values |  | 5880\ | 0\ |
| | [numeric] | min < med < max:\ | | | (100%) | (0%) |
| | | 111 < 118 < 333\ | | | | |
| | | IQR (CV) : 9 (0.4) | | | | |
+----+-----------------+--------------------------------+----------------------+---------------------+--------+---------+
| 4 | rei_name\ | 1\. Diet high in processed me\ | 392 ( 6.7%)\ |  | 5880\ | 0\ |
| | [character] | 2\. Diet high in red meat\ | 392 ( 6.7%)\ | | (100%) | (0%) |
| | | 3\. Diet high in sodium\ | 392 ( 6.7%)\ | | | |
| | | 4\. Diet high in sugar-sweete\ | 392 ( 6.7%)\ | | | |
| | | 5\. Diet high in trans fatty \ | 392 ( 6.7%)\ | | | |
| | | 6\. Diet low in calcium\ | 392 ( 6.7%)\ | | | |
| | | 7\. Diet low in fiber\ | 392 ( 6.7%)\ | | | |
| | | 8\. Diet low in fruits\ | 392 ( 6.7%)\ | | | |
| | | 9\. Diet low in legumes\ | 392 ( 6.7%)\ | | | |
| | | 10\. Diet low in milk\ | 392 ( 6.7%)\ | | | |
| | | [ 5 others ] | 1960 (33.3%) | | | |
+----+-----------------+--------------------------------+----------------------+---------------------+--------+---------+
| 5 | age_group_name\ | 1\. All Available Ages | 5880 (100.0%) |  | 5880\ | 0\ |
| | [character] | | | | (100%) | (0%) |
+----+-----------------+--------------------------------+----------------------+---------------------+--------+---------+
| 6 | sex\ | 1\. Female\ | 2940 (50.0%)\ |  | 5880\ | 0\ |
| | [character] | 2\. Male | 2940 (50.0%) | | (100%) | (0%) |
+----+-----------------+--------------------------------+----------------------+---------------------+--------+---------+
| 7 | parameter\ | 1\. continuous | 5880 (100.0%) |  | 5880\ | 0\ |
| | [character] | | | | (100%) | (0%) |
+----+-----------------+--------------------------------+----------------------+---------------------+--------+---------+
| 8 | mean\ | Mean (sd) : 34.8 (60.1)\ | 5880 distinct values |  | 5880\ | 0\ |
| | [numeric] | min < med < max:\ | | | (100%) | (0%) |
| | | 0 < 8.4 < 566.7\ | | | | |
| | | IQR (CV) : 42.2 (1.7) | | | | |
+----+-----------------+--------------------------------+----------------------+---------------------+--------+---------+
| 9 | upper\ | Mean (sd) : 37.9 (65.3)\ | 5880 distinct values |  | 5880\ | 0\ |
| | [numeric] | min < med < max:\ | | | (100%) | (0%) |
| | | 0 < 9.2 < 624.2\ | | | | |
| | | IQR (CV) : 46.1 (1.7) | | | | |
+----+-----------------+--------------------------------+----------------------+---------------------+--------+---------+
| 10 | lower\ | Mean (sd) : 31.9 (55.4)\ | 5880 distinct values |  | 5880\ | 0\ |
| | [numeric] | min < med < max:\ | | | (100%) | (0%) |
| | | 0 < 7.5 < 513.2\ | | | | |
| | | IQR (CV) : 38.8 (1.7) | | | | |
+----+-----------------+--------------------------------+----------------------+---------------------+--------+---------+
| 11 | unit\ | 1\. %energy/day\ | 784 (13.3%)\ |  | 5880\ | 0\ |
| | [character] | 2\. g/day | 5096 (86.7%) | | (100%) | (0%) |
+----+-----------------+--------------------------------+----------------------+---------------------+--------+---------+Let’s take a look at sep_age_diet_data.
| Name | sep_age_diet_data |
| Number of rows | 88200 |
| Number of columns | 11 |
| _______________________ | |
| Column type frequency: | |
| character | 6 |
| numeric | 5 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| location_name | 0 | 1 | 4 | 32 | 0 | 196 | 0 |
| rei_name | 0 | 1 | 16 | 39 | 0 | 15 | 0 |
| age_group_name | 0 | 1 | 7 | 8 | 0 | 15 | 0 |
| sex | 0 | 1 | 4 | 6 | 0 | 2 | 0 |
| parameter | 0 | 1 | 10 | 10 | 0 | 1 | 0 |
| unit | 0 | 1 | 5 | 11 | 0 | 2 | 0 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| year_id | 0 | 1 | 2017.00 | 0.00 | 2017 | 2017.00 | 2017.00 | 2017.00 | 2017.00 | ▁▁▇▁▁ |
| age_group_id | 0 | 1 | 32.87 | 54.46 | 10 | 13.00 | 17.00 | 30.00 | 235.00 | ▇▁▁▁▁ |
| mean | 0 | 1 | 39.84 | 63.86 | 0 | 0.56 | 10.80 | 52.63 | 604.01 | ▇▁▁▁▁ |
| upper | 0 | 1 | 48.07 | 78.38 | 0 | 0.70 | 13.55 | 59.96 | 806.98 | ▇▁▁▁▁ |
| lower | 0 | 1 | 33.46 | 54.54 | 0 | 0.42 | 8.46 | 44.87 | 639.06 | ▇▁▁▁▁ |
We can see that there are many more rows in this data set.
Let’s take a look at the different dietary risk factors considered. To do this we will use the distinct() function of the dplyr package.
This function grabs only the distinct or unique rows from a given variable (rei_name, in our case) of a given data frame (diet_data, in our case).
# A tibble: 15 x 1
rei_name
<chr>
1 Diet low in fruits
2 Diet low in vegetables
3 Diet low in whole grains
4 Diet low in nuts and seeds
5 Diet low in milk
6 Diet high in red meat
7 Diet high in processed meat
8 Diet high in sugar-sweetened beverages
9 Diet low in fiber
10 Diet low in seafood omega-3 fatty acids
11 Diet low in polyunsaturated fatty acids
12 Diet high in trans fatty acids
13 Diet high in sodium
14 Diet low in calcium
15 Diet low in legumes We will be using the %>% pipe for sequential steps in our code later on. This will make more sense when we have multiple sequential steps using the same data object.
We could do the same code as above using this notation. For example we first grab the diet_data, then we select the distinct values of the rei_name variable.
# A tibble: 15 x 1
rei_name
<chr>
1 Diet low in fruits
2 Diet low in vegetables
3 Diet low in whole grains
4 Diet low in nuts and seeds
5 Diet low in milk
6 Diet high in red meat
7 Diet high in processed meat
8 Diet high in sugar-sweetened beverages
9 Diet low in fiber
10 Diet low in seafood omega-3 fatty acids
11 Diet low in polyunsaturated fatty acids
12 Diet high in trans fatty acids
13 Diet high in sodium
14 Diet low in calcium
15 Diet low in legumes OK, so that gives us an idea of what dietary factors we can explore, and we can see that there are 15 of them.
Let’s see if the location_name values are the same between both CSV files. To do this we will use the setequal() function of dplyr.
[1] TRUEOK, we got the value of TRUE, so it looks like the same locations are in both files.
Note: In this case were comparing two different objects so using the pipe is not as useful.
Let’s take a look at the locations included in the data.
[1] "Global" "China"
[3] "North Korea" "Taiwan"
[5] "Cambodia" "Indonesia"
[7] "Laos" "Malaysia"
[9] "Maldives" "Myanmar"
[11] "Philippines" "Sri Lanka"
[13] "Thailand" "Timor-Leste"
[15] "Vietnam" "Fiji"
[17] "Kiribati" "Marshall Islands"
[19] "Federated States of Micronesia" "Papua New Guinea"
[21] "Samoa" "Solomon Islands"
[23] "Tonga" "Vanuatu"
[25] "Armenia" "Azerbaijan"
[27] "Georgia" "Kazakhstan"
[29] "Kyrgyzstan" "Mongolia"
[31] "Tajikistan" "Turkmenistan"
[33] "Uzbekistan" "Albania"
[35] "Bosnia and Herzegovina" "Bulgaria"
[37] "Croatia" "Czech Republic"
[39] "Hungary" "Macedonia"
[41] "Montenegro" "Poland"
[43] "Romania" "Serbia"
[45] "Slovakia" "Slovenia"
[47] "Belarus" "Estonia"
[49] "Latvia" "Lithuania"
[51] "Moldova" "Russian Federation"
[53] "Ukraine" "Brunei"
[55] "Japan" "South Korea"
[57] "Singapore" "Australia"
[59] "New Zealand" "Andorra"
[61] "Austria" "Belgium"
[63] "Cyprus" "Denmark"
[65] "Finland" "France"
[67] "Germany" "Greece"
[69] "Iceland" "Ireland"
[71] "Israel" "Italy"
[73] "Luxembourg" "Malta"
[75] "Netherlands" "Norway"
[77] "Portugal" "Spain"
[79] "Sweden" "Switzerland"
[81] "United Kingdom" "Argentina"
[83] "Chile" "Uruguay"
[85] "Canada" "United States"
[87] "Antigua and Barbuda" "The Bahamas"
[89] "Barbados" "Belize"
[91] "Cuba" "Dominica"
[93] "Dominican Republic" "Grenada"
[95] "Guyana" "Haiti"
[97] "Jamaica" "Saint Lucia"
[99] "Saint Vincent and the Grenadines" "Suriname"
[101] "Trinidad and Tobago" "Bolivia"
[103] "Ecuador" "Peru"
[105] "Colombia" "Costa Rica"
[107] "El Salvador" "Guatemala"
[109] "Honduras" "Mexico"
[111] "Nicaragua" "Panama"
[113] "Venezuela" "Brazil"
[115] "Paraguay" "Algeria"
[117] "Bahrain" "Egypt"
[119] "Iran" "Iraq"
[121] "Jordan" "Kuwait"
[123] "Lebanon" "Libya"
[125] "Morocco" "Palestine"
[127] "Oman" "Qatar"
[129] "Saudi Arabia" "Syria"
[131] "Tunisia" "Turkey"
[133] "United Arab Emirates" "Yemen"
[135] "Afghanistan" "Bangladesh"
[137] "Bhutan" "India"
[139] "Nepal" "Pakistan"
[141] "Angola" "Central African Republic"
[143] "Congo" "Democratic Republic of the Congo"
[145] "Equatorial Guinea" "Gabon"
[147] "Burundi" "Comoros"
[149] "Djibouti" "Eritrea"
[151] "Ethiopia" "Kenya"
[153] "Madagascar" "Malawi"
[155] "Mauritius" "Mozambique"
[157] "Rwanda" "Seychelles"
[159] "Somalia" "Tanzania"
[161] "Uganda" "Zambia"
[163] "Botswana" "Lesotho"
[165] "Namibia" "South Africa"
[167] "Swaziland" "Zimbabwe"
[169] "Benin" "Burkina Faso"
[171] "Cameroon" "Cape Verde"
[173] "Chad" "Cote d'Ivoire"
[175] "The Gambia" "Ghana"
[177] "Guinea" "Guinea-Bissau"
[179] "Liberia" "Mali"
[181] "Mauritania" "Niger"
[183] "Nigeria" "Sao Tome and Principe"
[185] "Senegal" "Sierra Leone"
[187] "Togo" "American Samoa"
[189] "Bermuda" "Greenland"
[191] "Guam" "Northern Mariana Islands"
[193] "Puerto Rico" "Virgin Islands, U.S."
[195] "South Sudan" "Sudan" OK, so there are global values, as well as values for 195 countries.
Let’s take a look at the data when we order it by the mean consumption rate column. We can do so using the arrange() function of the dplyr package.
Rows: 5,880
Columns: 11
$ year_id <dbl> 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, …
$ location_name <chr> "Lebanon", "Lebanon", "Italy", "Turkey", "Kazakhstan",…
$ rei_id <dbl> 123, 123, 123, 123, 123, 123, 123, 123, 123, 123, 123,…
$ rei_name <chr> "Diet high in trans fatty acids", "Diet high in trans …
$ age_group_name <chr> "All Available Ages", "All Available Ages", "All Avail…
$ sex <chr> "Male", "Female", "Male", "Male", "Male", "Male", "Mal…
$ parameter <chr> "continuous", "continuous", "continuous", "continuous"…
$ mean <dbl> 0.000727793, 0.000819837, 0.001357473, 0.001411079, 0.…
$ upper <dbl> 0.000892791, 0.001007998, 0.001491509, 0.001727481, 0.…
$ lower <dbl> 0.000585340, 0.000670438, 0.001233583, 0.001134972, 0.…
$ unit <chr> "%energy/day", "%energy/day", "%energy/day", "%energy/…OK, so it looks like people in Lebanon don’t eat very many trans fatty acids.
Let’s also figure out how many values there are in each age group of the data that is separated by age. We will use the count() function of the dplyr package to do this.
# A tibble: 15 x 2
age_group_name n
<chr> <int>
1 25 to 29 5880
2 30 to 34 5880
3 35 to 39 5880
4 40 to 44 5880
5 45 to 49 5880
6 50 to 54 5880
7 55 to 59 5880
8 60 to 64 5880
9 65 to 69 5880
10 70 to 74 5880
11 75 to 79 5880
12 80 to 84 5880
13 85 to 89 5880
14 90 to 94 5880
15 95 plus 5880That’s a lot of values!
Let’s look a bit deeper to try to understand why. We can use the count() function again but get the number of values for each category within sex, age_group_name and location_name of the data.
# A tibble: 5,880 x 4
sex age_group_name location_name n
<chr> <chr> <chr> <int>
1 Female 25 to 29 Afghanistan 15
2 Female 25 to 29 Albania 15
3 Female 25 to 29 Algeria 15
4 Female 25 to 29 American Samoa 15
5 Female 25 to 29 Andorra 15
6 Female 25 to 29 Angola 15
7 Female 25 to 29 Antigua and Barbuda 15
8 Female 25 to 29 Argentina 15
9 Female 25 to 29 Armenia 15
10 Female 25 to 29 Australia 15
# … with 5,870 more rowsOK, so it looks like these are probably the consumption values for each of the different dietary factors (since there were 15 different factors) for each age group and gender combination within each country.
We can confirm this by filtering the data to one of the age groups, for a single gender, and for a single location. To do this we can use the filter() function of the dplyr package. Notice that we need to use two equal signs == to specify what values we would like for each variable.
sep_age_diet_data %>%
dplyr::filter(sex == "Female",
age_group_name == "25 to 29",
location_name == "Afghanistan")# A tibble: 15 x 11
year_id location_name rei_name age_group_id age_group_name sex parameter
<dbl> <chr> <chr> <dbl> <chr> <chr> <chr>
1 2017 Afghanistan Diet lo… 10 25 to 29 Fema… continuo…
2 2017 Afghanistan Diet lo… 10 25 to 29 Fema… continuo…
3 2017 Afghanistan Diet lo… 10 25 to 29 Fema… continuo…
4 2017 Afghanistan Diet lo… 10 25 to 29 Fema… continuo…
5 2017 Afghanistan Diet lo… 10 25 to 29 Fema… continuo…
6 2017 Afghanistan Diet hi… 10 25 to 29 Fema… continuo…
7 2017 Afghanistan Diet hi… 10 25 to 29 Fema… continuo…
8 2017 Afghanistan Diet hi… 10 25 to 29 Fema… continuo…
9 2017 Afghanistan Diet lo… 10 25 to 29 Fema… continuo…
10 2017 Afghanistan Diet lo… 10 25 to 29 Fema… continuo…
11 2017 Afghanistan Diet lo… 10 25 to 29 Fema… continuo…
12 2017 Afghanistan Diet hi… 10 25 to 29 Fema… continuo…
13 2017 Afghanistan Diet hi… 10 25 to 29 Fema… continuo…
14 2017 Afghanistan Diet lo… 10 25 to 29 Fema… continuo…
15 2017 Afghanistan Diet lo… 10 25 to 29 Fema… continuo…
# … with 4 more variables: mean <dbl>, upper <dbl>, lower <dbl>, unit <chr>This confirms that for each of the 15 dietary factors, our unit of observation is a combination of gender, age and country. However, before we proceed with our analysis, we will want to perform some additional data wrangling. To do this, we will introduce the pdftools package, which will allow us to pull additional data from the manuscript itself.
While all of the mean consumption values are reported in grams, each dietary factor has a different amount that is considered optimal for consuming. To make the consumption values more comparable across factors, let’s also get some data from the PDF of the paper so that we can calculate consumption of these dietary factors as percentages of the daily optimum.
We are interested in this table on page 3:
First let’s import the PDF using the pfd_text() function of the pdftools package.
paper <- pdftools::pdf_text(here("docs",
"Afshin et al. 2019 - Health effects of dietary risks in 195 countries, ... 17 - a systematic analysis for the Global Burden of Disease Study 2017.pdf"))We can use the base summary() function to get a sense of what the data looks like. By base we mean that these functions are part of the base package and are loaded automatically on startup of R. Thus library(base) is not required.
Length Class Mode
15 character character We can see that we have 15 different character strings. Each one contains the text on each of the 15 different pages of the PDF.
Again, the table we are interested in is on the third page, so let’s grab just that portion of the PDF. The top of this page looks like:
Length Class Mode
1 character character chr " Articles\nin systolic blood pressure, and then estimated the Disease-specific deaths and disability-adjusted\nrelationship between change in systolic blood pressure life-years\nand disease outcomes.14 Data on disease-specific deaths and disability-adjusted\n life-years (DALYs) by age, sex, country, and year were\nOptimal level of intake "| __truncated__Here we can see that the pdf_table object now contains the text from the 3rd page as a single large character string. However the text is difficult to read because of the column structure in the PDF. Now let’s try to grab just the text in the table.
One way to approach this is to split the string by some pattern that we notice in the table.
All the rows of interest of the table appear to start with the word "Diet". Moreover, only the capitalized form of the word "Diet" appears to be within the table, and it is not present in the preceding text (although "diet" is).
Let’s use the str_split() function of the stringr package to split the data within the object called pdf_table by the word "Diet". Only lines from page 3 that contain the word "Diet" will be selected (and not "diet" as this function is case-sensitive). Each section of the text that contains "Diet" will be split into individual pieces every time the word "Diet" occurs and the word itself will be removed.
In this case we are also using the magrittr assignment pipe or double pipe that looks like this %<>% of the magrittr package. This allows us use the pdf_table data as input to the later steps but also reassign the output to the same data object name.
Using the base::summary() and dplyr::glimpse() function we can see that we created a list of the rows in the table that contained the word "Diet". We can see that we start with the row that contains "low in fruits".
Length Class Mode
[1,] 17 -none- characterList of 1
$ : chr [1:17] " "| __truncated__ " low in fruits Mean daily consumption of fruits (fresh, frozen, cooked, canned, or dried fruit"| __truncated__ " low in vegetables Mean daily consumption of vegetables (fresh, frozen, cooked, canned, or dried v"| __truncated__ " low in legumes Mean daily consumption of legumes (fresh, frozen, cooked, canned, or dried legu"| __truncated__ ...In order to extract the values that we want from these character strings, we will use some additional functions from the stringr package. RStudio creates really helpful cheat sheets like this one which shows you all the major functions in the stringr package. You can download others here.
You can see that we could have also used the str_split_fixed() function which would also separate the substrings into different columns of a matrix, however we would need to know the number of substrings or pieces that we would like returned.
For more information about str_split() see here.
Let’s separate the values within the list using the base unlist function, this will allow us to easily select the different substrings within the object called pdf_table.
It’s important to realize that the first split will split the text before the first occurrence of "Diet" as the first value in the output. (This is why there are 17 elements in pdf_table rather than 15, the number of rows in the table.) We could use the first() function of the dplyr package to look at this value. However, we will suppress the output as this is quite large.
Instead we can take a look at the second element of the list. using the nth() function of dplyr.
[1] " low in fruits Mean daily consumption of fruits (fresh, frozen, cooked, canned, or dried fruits, excluding 250 g (200–300) per day 94·9\n fruit juices and salted or pickled fruits)\n "Indeed this looks like the first row of interest in our table:
Using the last() and the nth() functions of the dplyr package we can take a look at the last values of the list.
#to see the second to last value we can use nth()
#the -2 specifies that we want the second-to-last value
#-3 would be third-to-last and -1 would be the last value
dplyr::nth(pdf_table, -2)[1] " high in sodium 24 h urinary sodium measured in g per day 3 g (1–5) per day* 26·2\n *To reflect the uncertainty in existing evidence on optimal level of intake for sodium, 1–5 g per day was considered as the uncertainty range for the optimal level of sodium where less than 2·3 g per day is the\n intake level of sodium associated with the lowest level of blood pressure in randomised controlled trials and 4–5 g per day is the level of sodium intake associated with the lowest risk of cardiovascular disease in\n observational studies.\n Table: "[1] "ary risk factor exposure definitions, optimal level, and data representativeness index, 1990–2017\nwww.thelancet.com Published online April 3, 2019 http://dx.doi.org/10.1016/S0140-6736(19)30041-8 3\n"
We don’t need this part of the table or the text before the table if we just want the consumption recommendations.
So we will select the second through the second-to-last of the substrings. Since we have seventeen substrings, we will select the second through the sixteenth. However a better way to do this rather than selecting by index, would be to select phrases that are unique to the text within the table that we want. We will use the str_subset() function of stringr package to select the table rows with consumption guidelines. Most of the rows have the phrase “Mean daily consumption”, however, there are other phrases for some of the rows, including “Mean daily intake” and “24 h sodium”. So we will subset for each of these phrases.
# one could subset the pdf_table like this:
#pdf_table <- pdf_table[2:16]
pdf_table %<>%
str_subset(pattern = "Mean daily consumption|Mean daily intake|24 h")Notice that we separate the different patterns to look for using vertical bar character "|" and that all of the patterns are within quotation marks together.
Question opportunity:
What other string patterns could you use to subset the rows of the table that we want?
Why might it be better to subset based on the text rather than the index?
Now the first row is what we want:
[1] " low in fruits Mean daily consumption of fruits (fresh, frozen, cooked, canned, or dried fruits, excluding 250 g (200–300) per day 94·9\n fruit juices and salted or pickled fruits)\n "And the last row is what we want:
[1] " high in sodium 24 h urinary sodium measured in g per day 3 g (1–5) per day* 26·2\n *To reflect the uncertainty in existing evidence on optimal level of intake for sodium, 1–5 g per day was considered as the uncertainty range for the optimal level of sodium where less than 2·3 g per day is the\n intake level of sodium associated with the lowest level of blood pressure in randomised controlled trials and 4–5 g per day is the level of sodium intake associated with the lowest risk of cardiovascular disease in\n observational studies.\n Table: "At this point, we have a better look at the current representation of the table data in R, and we might notice something that will need to be fixed. In the string above, the decimal points from the PDF are being recognized as something called an interpunct instead of a period or decimal. An interpunct is a centered dot, as opposed to a period or decimal that is aligned to the bottom of the line.
The interpunct was previously used to separate words in certain languages, like ancient Latin.
It is important to replace these for later when we want these values to be converted from character strings to numeric. We will again use the stringr package. This time we will use the str_replace_all() function which replaces all instances of a pattern in an individual string. In this case we want to replace all instances of the interpunct with a decimal point.
Looks good!
[1] " high in sodium 24 h urinary sodium measured in g per day 3 g (1–5) per day* 26.2\n *To reflect the uncertainty in existing evidence on optimal level of intake for sodium, 1–5 g per day was considered as the uncertainty range for the optimal level of sodium where less than 2.3 g per day is the\n intake level of sodium associated with the lowest level of blood pressure in randomised controlled trials and 4–5 g per day is the level of sodium intake associated with the lowest risk of cardiovascular disease in\n observational studies.\n Table: "Now we will try to split the strings for each row based on the presence of two spaces to create the columns of the table, as there appears to be more than one space between the columns. The resulting substrings will be separated by quotes.
For additional details, the second page of the stringr cheat sheet has more information about using “Special Characters” in stringr. For example \\s is interpreted as a space as the \\ indicates that the s should be interpreted as a special character and not simply the letter s. The {2,} indicates two or more spaces, while {2} would indicate exactly two spaces.
table_split <- str_split(string = pdf_table,
pattern = "\\s{2,}")
glimpse(table_split) #scroll the output!List of 15
$ : chr [1:6] " low in fruits" "Mean daily consumption of fruits (fresh, frozen, cooked, canned, or dried fruits, excluding" "250 g (200–300) per day" "94.9" ...
$ : chr [1:7] " low in vegetables" "Mean daily consumption of vegetables (fresh, frozen, cooked, canned, or dried vegetables," "360 g (290–430) per day" "94.9" ...
$ : chr [1:5] " low in legumes" "Mean daily consumption of legumes (fresh, frozen, cooked, canned, or dried legumes)" "60 g (50–70) per day" "94.9" ...
$ : chr [1:7] " low in whole grains" "Mean daily consumption of whole grains (bran, germ, and endosperm in their natural" "125 g (100–150) per day" "94.9" ...
$ : chr [1:5] " low in nuts and seeds" "Mean daily consumption of nut and seed foods" "21 g (16–25) per day" "94.9" ...
$ : chr [1:6] " low in milk" "Mean daily consumption of milk including non-fat, low-fat, and full-fat milk, excluding soy" "435 g (350–520) per day" "94.9" ...
$ : chr [1:6] " high in red meat" "Mean daily consumption of red meat (beef, pork, lamb, and goat, but excluding poultry, fish," "23 g (18–27) per day" "94.9" ...
$ : chr [1:6] " high in processed meat" "Mean daily consumption of meat preserved by smoking, curing, salting, or addition of" "2 g (0–4) per day" "36.9" ...
$ : chr [1:6] " high in sugar-sweetened Mean daily consumption of beverages with ≥50 kcal per 226.8 serving, including carbonated" "3 g (0–5) per day" "36.9" "beverages" ...
$ : chr [1:6] " low in fibre" "Mean daily intake of fibre from all sources including fruits, vegetables, grains, legumes, and" "24 g (19–28) per day" "94.9" ...
$ : chr [1:5] " low in calcium" "Mean daily intake of calcium from all sources, including milk, yogurt, and cheese" "1.25 g (1.00–1.50) per day" "94.9" ...
$ : chr [1:5] " low in seafood omega-3 Mean daily intake of eicosapentaenoic acid and docosahexaenoic acid" "250 mg (200–300) per day" "94.9" "fatty acids" ...
$ : chr [1:7] " low in polyunsaturated" "Mean daily intake of omega-6 fatty acids from all sources, mainly liquid vegetable oils," "11% (9–13) of total daily energy" "94.9" ...
$ : chr [1:6] " high in trans fatty acids" "Mean daily intake of trans fat from all sources, mainly partially hydrogenated vegetable oils" "0.5% (0.0–1.0) of total daily energy" "36.9" ...
$ : chr [1:8] " high in sodium" "24 h urinary sodium measured in g per day" "3 g (1–5) per day*" "26.2" ...Now we can see that each of our 15 strings has been split into pieces, but unfortunately, it was not completely consistent across dietary factors. Why did this happen? If we look closely, we can see that the sugar-sweetened beverage and the seafood category had only one space between the first and second columns. These are the columns about the dietary category and the one that describes in more detail what the consumption suggestion is about.
The values for these two columns appear to be together still in the same substring for these two categories. We can see this because there are no quotation marks adjacent to the word "Mean".
Here you can see how the next substring should have started with the word "Mean" by the new inclusion of a quotation mark ". The red rectangles indicate the problematic substrings, while the green rectangles show examples where the split worked correctly.
We can add an extra space in front of the word "Mean" for these particular categories and then try splitting again.
Since we originally split based on two or more spaces, we can just add a space in front of the word “Mean” for all the pdf_table strings and then try subsetting again. We can use the str_which() function of the stringr package to find the index of these particular cases.
[1] 9 12Here we can use this function with [] to see just the strings that match these patterns within pdf_table:
[1] " high in sugar-sweetened Mean daily consumption of beverages with ≥50 kcal per 226.8 serving, including carbonated 3 g (0–5) per day 36.9\n beverages beverages, sodas, energy drinks, fruit drinks, but excluding 100% fruit and vegetable juices\n "
[2] " low in seafood omega-3 Mean daily intake of eicosapentaenoic acid and docosahexaenoic acid 250 mg (200–300) per day 94.9\n fatty acids\n " Now we can replace these values within the pdf_table object after adding a space in front of “Mean”:
pdf_table[str_which(pdf_table,
pattern =
"seafood|sugar")] <- str_replace(
string = pdf_table[str_which(pdf_table,
pattern =
"seafood|sugar")],
pattern = "Mean",
replacement = " Mean")And now we can try splitting again by two or more spaces:
We could also just add a space in front of all the values of “Mean” in pdf_table since the split was performed based on two or more spaces. Thus the other elements in pdf_table would also be split just as before despite the additional space.
List of 15
$ : chr [1:6] " low in fruits" "Mean daily consumption of fruits (fresh, frozen, cooked, canned, or dried fruits, excluding" "250 g (200–300) per day" "94.9" ...
$ : chr [1:7] " low in vegetables" "Mean daily consumption of vegetables (fresh, frozen, cooked, canned, or dried vegetables," "360 g (290–430) per day" "94.9" ...
$ : chr [1:5] " low in legumes" "Mean daily consumption of legumes (fresh, frozen, cooked, canned, or dried legumes)" "60 g (50–70) per day" "94.9" ...
$ : chr [1:7] " low in whole grains" "Mean daily consumption of whole grains (bran, germ, and endosperm in their natural" "125 g (100–150) per day" "94.9" ...
$ : chr [1:5] " low in nuts and seeds" "Mean daily consumption of nut and seed foods" "21 g (16–25) per day" "94.9" ...
$ : chr [1:6] " low in milk" "Mean daily consumption of milk including non-fat, low-fat, and full-fat milk, excluding soy" "435 g (350–520) per day" "94.9" ...
$ : chr [1:6] " high in red meat" "Mean daily consumption of red meat (beef, pork, lamb, and goat, but excluding poultry, fish," "23 g (18–27) per day" "94.9" ...
$ : chr [1:6] " high in processed meat" "Mean daily consumption of meat preserved by smoking, curing, salting, or addition of" "2 g (0–4) per day" "36.9" ...
$ : chr [1:7] " high in sugar-sweetened" "Mean daily consumption of beverages with ≥50 kcal per 226.8 serving, including carbonated" "3 g (0–5) per day" "36.9" ...
$ : chr [1:6] " low in fibre" "Mean daily intake of fibre from all sources including fruits, vegetables, grains, legumes, and" "24 g (19–28) per day" "94.9" ...
$ : chr [1:5] " low in calcium" "Mean daily intake of calcium from all sources, including milk, yogurt, and cheese" "1.25 g (1.00–1.50) per day" "94.9" ...
$ : chr [1:6] " low in seafood omega-3" "Mean daily intake of eicosapentaenoic acid and docosahexaenoic acid" "250 mg (200–300) per day" "94.9" ...
$ : chr [1:7] " low in polyunsaturated" "Mean daily intake of omega-6 fatty acids from all sources, mainly liquid vegetable oils," "11% (9–13) of total daily energy" "94.9" ...
$ : chr [1:6] " high in trans fatty acids" "Mean daily intake of trans fat from all sources, mainly partially hydrogenated vegetable oils" "0.5% (0.0–1.0) of total daily energy" "36.9" ...
$ : chr [1:8] " high in sodium" "24 h urinary sodium measured in g per day" "3 g (1–5) per day*" "26.2" ...Looks better!
We want just the first (the food category) and third column (the optimal consumption amount suggested) for each row in the table. However, the table is current stored as a list of character vectors, so it is not quite so simple to extract these values.
We can use the map function of the purrr package to accomplish this.
The map function allows us to perform the same action multiple times across each element within an object, in this case, a list.
The following will allow us to select the first or third substring from each element of the pdf_table object.
[[1]]
[1] " low in fruits"
[[2]]
[1] " low in vegetables"
[[3]]
[1] " low in legumes"
[[4]]
[1] " low in whole grains"
[[5]]
[1] " low in nuts and seeds"
[[6]]
[1] " low in milk"[[1]]
[1] "250 g (200–300) per day"
[[2]]
[1] "360 g (290–430) per day"
[[3]]
[1] "60 g (50–70) per day"
[[4]]
[1] "125 g (100–150) per day"
[[5]]
[1] "21 g (16–25) per day"
[[6]]
[1] "435 g (350–520) per day"Now we will create a tibble using this data. However, currently both category and amount are of class list. To create a tibble we need to unlist the data to create vectors.
[1] "list"[1] "character" [1] " low in fruits" " low in vegetables"
[3] " low in legumes" " low in whole grains"
[5] " low in nuts and seeds" " low in milk"
[7] " high in red meat" " high in processed meat"
[9] " high in sugar-sweetened" " low in fibre"
[11] " low in calcium" " low in seafood omega-3"
[13] " low in polyunsaturated" " high in trans fatty acids"
[15] " high in sodium" [1] "250 g (200–300) per day"
[2] "360 g (290–430) per day"
[3] "60 g (50–70) per day"
[4] "125 g (100–150) per day"
[5] "21 g (16–25) per day"
[6] "435 g (350–520) per day"
[7] "23 g (18–27) per day"
[8] "2 g (0–4) per day"
[9] "3 g (0–5) per day"
[10] "24 g (19–28) per day"
[11] "1.25 g (1.00–1.50) per day"
[12] "250 mg (200–300) per day"
[13] "11% (9–13) of total daily energy"
[14] "0.5% (0.0–1.0) of total daily energy"
[15] "3 g (1–5) per day*" We could have done all of this at once in one command like this:
Now we will create a tibble, which is an important data frame structure in the tidyverse which allows us to use other packages in the tidyverse with our data.
We will name our tibble columns now as we create our tibble using the tibble() function of both the tidyr and the tibble packages, as names are required in tibbles.
# A tibble: 15 x 2
category amount
<chr> <chr>
1 " low in fruits" 250 g (200–300) per day
2 " low in vegetables" 360 g (290–430) per day
3 " low in legumes" 60 g (50–70) per day
4 " low in whole grains" 125 g (100–150) per day
5 " low in nuts and seeds" 21 g (16–25) per day
6 " low in milk" 435 g (350–520) per day
7 " high in red meat" 23 g (18–27) per day
8 " high in processed meat" 2 g (0–4) per day
9 " high in sugar-sweetened" 3 g (0–5) per day
10 " low in fibre" 24 g (19–28) per day
11 " low in calcium" 1.25 g (1.00–1.50) per day
12 " low in seafood omega-3" 250 mg (200–300) per day
13 " low in polyunsaturated" 11% (9–13) of total daily energy
14 " high in trans fatty acids" 0.5% (0.0–1.0) of total daily energy
15 " high in sodium" 3 g (1–5) per day* Looking pretty good!
Recall that the main goal of this data wrangling is to extract the optimal intake level for each dietary factor. So while we have managed to pull and organize the data from the pdf table, we need to further process the results to isolate this numeric value.
Do to this, we want to separate the different numbers within the amount column, to isolate the optimal amount, and the optimal range, and eventually convert them to numeric values.
Recall what the original table looked like:
We can use the tidyr::separate() function to separate the data within the amount column into three new columns based on the optimal level and the optimal range. We can separate the values based on the open parentheses "(" and the long dash "–" characters. Again we will use the bar "|" to indicate that we want to separate by either character.
# The first column will be called optimal
# It will contain the 1st part of the amount column data before the "("
# The 2nd column will be called lower
# It will contain the data after the "("
# The 3rd column will be called upper
# It will contain the 2nd part of the data based on the "–"
# The "[]" are necessary - we will explain very soon
guidelines %<>%
tidyr::separate(amount,
c("optimal", "lower", "upper"),
sep = "[(|–]")
guidelines# A tibble: 15 x 4
category optimal lower upper
<chr> <chr> <chr> <chr>
1 " low in fruits" "250 g " 200 300) per day
2 " low in vegetables" "360 g " 290 430) per day
3 " low in legumes" "60 g " 50 70) per day
4 " low in whole grains" "125 g " 100 150) per day
5 " low in nuts and seeds" "21 g " 16 25) per day
6 " low in milk" "435 g " 350 520) per day
7 " high in red meat" "23 g " 18 27) per day
8 " high in processed meat" "2 g " 0 4) per day
9 " high in sugar-sweetened" "3 g " 0 5) per day
10 " low in fibre" "24 g " 19 28) per day
11 " low in calcium" "1.25 g " 1.00 1.50) per day
12 " low in seafood omega-3" "250 mg " 200 300) per day
13 " low in polyunsaturated" "11% " 9 13) of total daily energy
14 " high in trans fatty acids" "0.5% " 0.0 1.0) of total daily energy
15 " high in sodium" "3 g " 1 5) per day* Let’s also create a new variable/column in our tibble that indicates the direction of over- or under-consumption that can be harmful for each dietary factor.
# A tibble: 15 x 5
direction food optimal lower upper
<chr> <chr> <chr> <chr> <chr>
1 " low" fruits "250 g " 200 300) per day
2 " low" vegetables "360 g " 290 430) per day
3 " low" legumes "60 g " 50 70) per day
4 " low" whole grains "125 g " 100 150) per day
5 " low" nuts and seeds "21 g " 16 25) per day
6 " low" milk "435 g " 350 520) per day
7 " high" red meat "23 g " 18 27) per day
8 " high" processed meat "2 g " 0 4) per day
9 " high" sugar-sweetened "3 g " 0 5) per day
10 " low" fibre "24 g " 19 28) per day
11 " low" calcium "1.25 g " 1.00 1.50) per day
12 " low" seafood omega-3 "250 mg " 200 300) per day
13 " low" polyunsaturated "11% " 9 13) of total daily energy
14 " high" trans fatty acids "0.5% " 0.0 1.0) of total daily energy
15 " high" sodium "3 g " 1 5) per day* If we wanted to remove the direction variable we could use the modify_at() function of the purrr package:
OK, looking better, but we still need a bit of cleaning to remove symbols and extra words from the columns. Some of the extra symbols include: "%", ")" and the "*".
The "*" and the ")" are what we call metacharacters or regular expressions. These are characters that have special meanings.
Now we need the "\\" to indicate that we want these characters to be matched exactly and not interpreted as the meaning of the symbol. Previously we used [] around the characters when we separated the optimal amount columns. Recall that we used "[(|–]". This worked because these are punctuation characters, we could have also used "\\(|–".
See here for more info about regular expressions in R.
Also here we have a bit of an example using the str_count() function of stringr, which counts the number of instances of a character string. In this case we will look for individual characters but you could also search for words or phrases.
[1] "Testing for ts or\ttabs can be tricky.(yes, it really can!*)\n"Count the letter t:
[1] 5Count tabs:
[1] 1[1] 5Count parentheses:
# this would not work because r thinks this is part of the code itself
# str_count(regextest, ")")
# this would not work because r thinks this is part of the code itself
# str_count(regextest, "\)")
str_count(regextest, "\\)") # this works![1] 1[1] 1Count the occurrence of the asterix:
# this also does not work
# str_count(regextest, "*")
# nor does this
# str_count(regextest, "\*")
str_count(regextest, "\\*") # this works![1] 1[1] 1We also want to make a unit variable so that we can make sure that our units are consistent later.
[1] "250 g " "360 g " "60 g " "125 g " "21 g " "435 g " "23 g "
[8] "2 g " "3 g " "24 g " "1.25 g " "250 mg " "11% " "0.5% "
[15] "3 g " Notice that the values that are percentages don’t have spaces between the number and the unit. We can separate the "optimal" values by a space or a percent symbol "%" using "|" to indicate that we want to separate by either. In this case we will lose the “%” and will need to add it back to those values.
guidelines %<>%
separate(optimal, into = c("optimal", "unit"),
sep = " |%",
remove = FALSE)
guidelines# A tibble: 15 x 6
direction food lower optimal unit upper
<chr> <chr> <chr> <chr> <chr> <chr>
1 " low" fruits 200 250 "g" 300) per day
2 " low" vegetables 290 360 "g" 430) per day
3 " low" legumes 50 60 "g" 70) per day
4 " low" whole grains 100 125 "g" 150) per day
5 " low" nuts and seeds 16 21 "g" 25) per day
6 " low" milk 350 435 "g" 520) per day
7 " high" red meat 18 23 "g" 27) per day
8 " high" processed meat 0 2 "g" 4) per day
9 " high" sugar-sweetened 0 3 "g" 5) per day
10 " low" fibre 19 24 "g" 28) per day
11 " low" calcium 1.00 1.25 "g" 1.50) per day
12 " low" seafood omega-3 200 250 "mg" 300) per day
13 " low" polyunsaturated 9 11 "" 13) of total daily energy
14 " high" trans fatty acids 0.0 0.5 "" 1.0) of total daily energy
15 " high" sodium 1 3 "g" 5) per day* Great, so to now we will add “%” to the unit variable for the "low in polyunsaturated" and "high in trans fatty acids" rows.
First we need to replace the empty values with NA using the na_if() function of the dplyr package.
# A tibble: 15 x 6
direction food lower optimal unit upper
<chr> <chr> <chr> <chr> <chr> <chr>
1 " low" fruits 200 250 g 300) per day
2 " low" vegetables 290 360 g 430) per day
3 " low" legumes 50 60 g 70) per day
4 " low" whole grains 100 125 g 150) per day
5 " low" nuts and seeds 16 21 g 25) per day
6 " low" milk 350 435 g 520) per day
7 " high" red meat 18 23 g 27) per day
8 " high" processed meat 0 2 g 4) per day
9 " high" sugar-sweetened 0 3 g 5) per day
10 " low" fibre 19 24 g 28) per day
11 " low" calcium 1.00 1.25 g 1.50) per day
12 " low" seafood omega-3 200 250 mg 300) per day
13 " low" polyunsaturated 9 11 <NA> 13) of total daily energy
14 " high" trans fatty acids 0.0 0.5 <NA> 1.0) of total daily energy
15 " high" sodium 1 3 g 5) per day* Then to replace the NA values, we can use the replace_na() function in the tidyr package and the mutate() function of dplyr to specify which values to replace, in this case the NA values within the variable unit. Essentially this variable gets reassigned with the new values, as we mostly think of the mutate() function as creating new variables.
guidelines %<>%
dplyr::mutate(unit = replace_na(unit, "%"))
# now just to show these rows
guidelines %>%
filter(unit == "%")# A tibble: 2 x 6
direction food lower optimal unit upper
<chr> <chr> <chr> <chr> <chr> <chr>
1 " low" polyunsaturated 9 11 % 13) of total daily energy
2 " high" trans fatty acids 0.0 0.5 % 1.0) of total daily energyLet’s also move unit to be the last column. We can use the select() and everything() functions of the dplyr package to do this.
Here you can see Hadley Wickham’s (Chief Scientist at RStudio) explanation for this behavior of select():
https://github.com/tidyverse/dplyr/issues/2838#issuecomment-306062800
To remove all of the remaining extra characters and words we will again use the stringr package. This time we will use the str_remove_all() function to remove all instances of these characters.
guidelines <- as_tibble(
map(guidelines, str_remove_all,
pattern = "\\) per day|\\) of total daily energy|\\*"))Nice! that’s pretty clean but we can do a bit more.
One of the next things to notice about our data is all of our variables are of class character, which is not how we want them to be.
For example, the optimal amounts of consumption are currently of class character, which is indicated by the <chr> just below the column names/variable names of the guidelines tibble:
# A tibble: 15 x 6
direction food lower optimal upper unit
<chr> <chr> <chr> <chr> <chr> <chr>
1 " low" fruits 200 250 300 g
2 " low" vegetables 290 360 430 g
3 " low" legumes 50 60 70 g
4 " low" whole grains 100 125 150 g
5 " low" nuts and seeds 16 21 25 g
6 " low" milk 350 435 520 g
7 " high" red meat 18 23 27 g
8 " high" processed meat 0 2 4 g
9 " high" sugar-sweetened 0 3 5 g
10 " low" fibre 19 24 28 g
11 " low" calcium 1.00 1.25 1.50 g
12 " low" seafood omega-3 200 250 300 mg
13 " low" polyunsaturated 9 11 13 %
14 " high" trans fatty acids 0.0 0.5 1.0 %
15 " high" sodium 1 3 5 g To convert these values to numeric we can use the mutate_at() function of the dplyr package.
The mutate_at() function allows us to perform a function on specific columns/variables within a tibble. We need to indicate which variables we would like to convert using vars(). In this case if we look at the beginning of the guidelines tibble, we can see that optimal, lower and upper should be converted. As these three columns are sequential, we can simply put a : between optimal and upper to indicate that we want all the variables in between these columns to be converted.
# A tibble: 15 x 6
direction food lower optimal upper unit
<chr> <chr> <dbl> <dbl> <dbl> <chr>
1 " low" fruits 200 250 300 g
2 " low" vegetables 290 360 430 g
3 " low" legumes 50 60 70 g
4 " low" whole grains 100 125 150 g
5 " low" nuts and seeds 16 21 25 g
6 " low" milk 350 435 520 g
7 " high" red meat 18 23 27 g
8 " high" processed meat 0 2 4 g
9 " high" sugar-sweetened 0 3 5 g
10 " low" fibre 19 24 28 g
11 " low" calcium 1 1.25 1.5 g
12 " low" seafood omega-3 200 250 300 mg
13 " low" polyunsaturated 9 11 13 %
14 " high" trans fatty acids 0 0.5 1 %
15 " high" sodium 1 3 5 g Great! Now these variables are of class <dbl> (stands for double) which indicates that they are numeric. Here is a link for more information on numeric classes in R.
If we had not replaced the "·" interpunct values to a period, conversion from character to numeric would be problematic and would result in NA values.
We seem to have lost the word "beverages" from the "sugar-sweetened beverages" category, as well as "fatty acids" from the "seafood omega 3 fatty acids", and the "polyunsaturated fatty acids" categories as the full category name was listed on two lines within the table. We would like to replace these values with the full name.
To select the food variable we will show you several options. Only a couple will work well with reassigning the data in that particular variable within guidelines without assigning an intermediate data object. We will look using mutate_at(), pull(), select(), and two styles of brackets ["variable name"] and [["variablename"]].
The bracket ["variable name"] option and the select() option will grab a tibble (data frame) version of the food column out of guidelines. However we can’t start commands with select for assignments.
# A tibble: 15 x 1
food
<chr>
1 fruits
2 vegetables
3 legumes
4 whole grains
5 nuts and seeds
6 milk
7 red meat
8 processed meat
9 sugar-sweetened
10 fibre
11 calcium
12 seafood omega-3
13 polyunsaturated
14 trans fatty acids
15 sodium # A tibble: 15 x 1
food
<chr>
1 fruits
2 vegetables
3 legumes
4 whole grains
5 nuts and seeds
6 milk
7 red meat
8 processed meat
9 sugar-sweetened
10 fibre
11 calcium
12 seafood omega-3
13 polyunsaturated
14 trans fatty acids
15 sodium pull() and the bracket [["variable name"]] option in contrast, will grab the vector version of the food data:
[1] "fruits" "vegetables" "legumes"
[4] "whole grains" "nuts and seeds" "milk"
[7] "red meat" "processed meat" "sugar-sweetened"
[10] "fibre" "calcium" "seafood omega-3"
[13] "polyunsaturated" "trans fatty acids" "sodium" [1] "fruits" "vegetables" "legumes"
[4] "whole grains" "nuts and seeds" "milk"
[7] "red meat" "processed meat" "sugar-sweetened"
[10] "fibre" "calcium" "seafood omega-3"
[13] "polyunsaturated" "trans fatty acids" "sodium" The pull() function can be very useful when combined with other functions (for example you typically want to use a vector with the str_replace() function), but just like select, we can’t start assignments with pull().
This is not possible and will result in an error:
select(guidelines, food) <-
str_replace(
pull(guidelines,"food"),
pattern = "sugar-sweetened",
replacement = "sugar-sweetened beverages")
guidelines %>% select(food) <-
str_replace(
pull(guidelines,"food"),
pattern = "sugar-sweetened",
replacement = "sugar-sweetened beverages")This will only print the result, but not reassign the food variable values:
guidelines %>%
pull(food) %>%
str_replace(
pattern = "sugar-sweetened",
replacement = "sugar-sweetened beverages") [1] "fruits" "vegetables"
[3] "legumes" "whole grains"
[5] "nuts and seeds" "milk"
[7] "red meat" "processed meat"
[9] "sugar-sweetened beverages" "fibre"
[11] "calcium" "seafood omega-3"
[13] "polyunsaturated" "trans fatty acids"
[15] "sodium" Using select() would work as well to print the result (although the result structure is different):
guidelines %>%
select(food) %>%
str_replace(
pattern = "sugar-sweetened",
replacement = "sugar-sweetened beverages")[1] "c(\"fruits\", \"vegetables\", \"legumes\", \"whole grains\", \"nuts and seeds\", \"milk\", \"red meat\", \"processed meat\", \"sugar-sweetened beverages\", \"fibre\", \"calcium\", \"seafood omega-3\", \"polyunsaturated\", \"trans fatty acids\", \"sodium\")"Question opportunity:
Why do these commands not reassign the food variable values?
The bracket option is great alternative and allows us to reassign the values within guidelines easily. Either of the two styles of brackets: ["variable name"] and [["variablename"]] will work.
# 1st method: `["variable name"]`
# Replacing "sugar-sweetened" with "sugar-sweetened beverages"
guidelines["food"] <-
str_replace(
pull(guidelines,"food"),
pattern = "sugar-sweetened",
replacement = "sugar-sweetened beverages")
# 2nd method: `[["variablename"]]`
# Replacing "seafood omega-3" with"seafood omega-3 fatty acids"
guidelines[["food"]] <-
str_replace(
pull(guidelines,"food"),
pattern = "seafood omega-3",
replacement = "seafood omega-3 fatty acids")
guidelines# A tibble: 15 x 6
direction food lower optimal upper unit
<chr> <chr> <dbl> <dbl> <dbl> <chr>
1 " low" fruits 200 250 300 g
2 " low" vegetables 290 360 430 g
3 " low" legumes 50 60 70 g
4 " low" whole grains 100 125 150 g
5 " low" nuts and seeds 16 21 25 g
6 " low" milk 350 435 520 g
7 " high" red meat 18 23 27 g
8 " high" processed meat 0 2 4 g
9 " high" sugar-sweetened beverages 0 3 5 g
10 " low" fibre 19 24 28 g
11 " low" calcium 1 1.25 1.5 g
12 " low" seafood omega-3 fatty acids 200 250 300 mg
13 " low" polyunsaturated 9 11 13 %
14 " high" trans fatty acids 0 0.5 1 %
15 " high" sodium 1 3 5 g Finally, the best option is probably the mutate_at() function from dplyr. In this case we need to include ~ in front of the function that we would like to use on the values in our food variables. We also include . as a replacement to reference the data that we want to use within str_replace() (which in this case is the food variable values of guidelines).
Notice we didn’t need this when we previously use mutate_at() with the as.numeric() function. This is because the str_replace() function requires us to specify what data we are using as one of the arguments, while as.numeric() does not.
# Replacing "polyunsaturated" with"polyunsaturated fatty acids"
guidelines %<>%
mutate_at(vars(food),
~str_replace(
string = .,
pattern = "polyunsaturated",
replacement = "polyunsaturated fatty acids"))
guidelines# A tibble: 15 x 6
direction food lower optimal upper unit
<chr> <chr> <dbl> <dbl> <dbl> <chr>
1 " low" fruits 200 250 300 g
2 " low" vegetables 290 360 430 g
3 " low" legumes 50 60 70 g
4 " low" whole grains 100 125 150 g
5 " low" nuts and seeds 16 21 25 g
6 " low" milk 350 435 520 g
7 " high" red meat 18 23 27 g
8 " high" processed meat 0 2 4 g
9 " high" sugar-sweetened beverages 0 3 5 g
10 " low" fibre 19 24 28 g
11 " low" calcium 1 1.25 1.5 g
12 " low" seafood omega-3 fatty acids 200 250 300 mg
13 " low" polyunsaturated fatty acids 9 11 13 %
14 " high" trans fatty acids 0 0.5 1 %
15 " high" sodium 1 3 5 g This might be considered a better option because it is more readable as to where the food data came from that we are replacing values within.
There is one last minor detail… the direction variable has leading spaces still. We can use str_trim() to fix that!
# A tibble: 15 x 6
direction food lower optimal upper unit
<chr> <chr> <dbl> <dbl> <dbl> <chr>
1 low fruits 200 250 300 g
2 low vegetables 290 360 430 g
3 low legumes 50 60 70 g
4 low whole grains 100 125 150 g
5 low nuts and seeds 16 21 25 g
6 low milk 350 435 520 g
7 high red meat 18 23 27 g
8 high processed meat 0 2 4 g
9 high sugar-sweetened beverages 0 3 5 g
10 low fibre 19 24 28 g
11 low calcium 1 1.25 1.5 g
12 low seafood omega-3 fatty acids 200 250 300 mg
13 low polyunsaturated fatty acids 9 11 13 %
14 high trans fatty acids 0 0.5 1 %
15 high sodium 1 3 5 g OK! Now we know how much of each dietary factor we generally need for optimal health according to the guidelines used in this article.
Recall that the main goal of pulling the guideline amounts from the pdf was that we would like to see how the mean consumption rates for the different groups of people compared to the optimal intake guidelines.
One way we could do this is to calculate a consumption percentage of the optimal value.
To calculate this it would be helpful to put the guideline amounts with the average consumption rates into the same tibble, especially because the observed consumption data (diet_data and sep_age_diet_data) are very different dimensions from the guidelines data.
In order to create a tibble with our observed consumption rates with the suggested consumption rates, we will join our data using dplyr. In order to do so it is important that our different data sets have at least one column with the same values that we can use to join them together. So let’s first assess if that is the case.
# A tibble: 15 x 1
rei_name
<chr>
1 Diet low in fruits
2 Diet low in vegetables
3 Diet low in whole grains
4 Diet low in nuts and seeds
5 Diet low in milk
6 Diet high in red meat
7 Diet high in processed meat
8 Diet high in sugar-sweetened beverages
9 Diet low in fiber
10 Diet low in seafood omega-3 fatty acids
11 Diet low in polyunsaturated fatty acids
12 Diet high in trans fatty acids
13 Diet high in sodium
14 Diet low in calcium
15 Diet low in legumes # A tibble: 15 x 1
food
<chr>
1 fruits
2 vegetables
3 legumes
4 whole grains
5 nuts and seeds
6 milk
7 red meat
8 processed meat
9 sugar-sweetened beverages
10 fibre
11 calcium
12 seafood omega-3 fatty acids
13 polyunsaturated fatty acids
14 trans fatty acids
15 sodium We are actually pretty close: there are 15 dietary factors in each data set, and the names are nearly the same. To make them match completely, we can see that we need to remove the "Diet low in" and "Diet high in" phrases from the observed consumption data.
diet_data %<>%
mutate_at(vars(rei_name),
~ str_remove(string = .,
pattern = "Diet low in |Diet high in "))
sep_age_diet_data %<>%
mutate_at(vars(rei_name),
~ str_remove(string = .,
pattern = "Diet low in |Diet high in "))Also let’s double check that the two observed files have the same exact values for dietary factor names.
We can use the setequal() function from dplyr to check that the unique values for rei_name are the same for both diet_data and sep_age_diet_data.
[1] TRUEGreat!
Note that the default of the set_equal function ignores the order of values in rows. So we still don’t know if the order is the same.
We can check using the all_equal function of dplyr which reports back clues about what might be different if anything. Importantly we are including ignore_row_order = FALSE as the default is TRUE.
all_equal(distinct(diet_data, rei_name),
distinct(sep_age_diet_data, rei_name),
ignore_row_order = FALSE)[1] TRUELooks like they are also in the same order.
Note that if any of the values are different, all_equal() will first report this and will not report that the rows are in a different order.
Here is a toy example about how the three comparison functions(setequal(), all_equal() (also all.equal() for tbl_df), and setdiff()) work in dplyr.
It’s important to realize that row order is ignored by bothsetequal() and setdiff().
Now let’s compare two tibbles that have different row orders and different values.
Here are our tibbles to compare:
# A tibble: 4 x 1
test
<chr>
1 A
2 B
3 AC
4 D # A tibble: 4 x 1
test
<chr>
1 A
2 D
3 A
4 B [1] "tbl_df" "tbl" "data.frame"Since we are using tibbles, which are of class tbl_df we can use either all_equal or all.equal(). Notice that it doesn’t report rows being a different order becuase it first tells what rows have unique values or rows with a value that has a different number of frequency.
[1] "- Rows in x but not in y: 3\n"[1] "- Rows in x but not in y: 3\n"[1] "Component \"test\": 3 string mismatches"[1] "Component \"test\": 3 string mismatches"setequal() does not provide clues about what is different but TRUE (no differences) or FALSE (indicating at least one difference).
[1] FALSEsetdiff() tells us what is different and is dependent on the order of the objects compared, but prioritizes the values that are unique to each.
# A tibble: 1 x 1
test
<chr>
1 AC # A tibble: 0 x 1
# … with 1 variable: test <chr>Now let’s make it so that only the order is different:
# A tibble: 4 x 1
test
<chr>
1 A
2 B
3 AC
4 D # A tibble: 4 x 1
test
<chr>
1 A
2 D
3 AC
4 B Now that there are no values that are unique to either X or Y, all_equal() reports that there is a different order.
[1] TRUE[1] "Same row values, but different order"Remember setequal() ignores order and gives a value of TRUE for no differences.
[1] TRUEsetdiff() also ignores order and shows no differences.
# A tibble: 0 x 1
# … with 1 variable: test <chr>If we have different column/variable names this makes comparisons more challenging. Columns will be identified for having different names.
all_equal() will simply report that col names are different
[1] "not compatible: \n- Cols in y but not x: `colname2`.\n- Cols in x but not y: `colname1`.\n"[1] "not compatible: \n- Cols in y but not x: `colname2`.\n- Cols in x but not y: `colname1`.\n"seteqaul() will report TRUE or FALSE to indicate either a difference in columns or rows
[1] FALSEsetdiff() requires that columnames be the same so this will cause an error
OK, let’s keep going with our data.
How similar are the guidelines tibble and the observed consumption tibbles?
[1] FALSEOK, looks like we have some different values.
Let’s use the setdiff function to get more information about what is different between the values.
:( That wont work. This is because setdiff() requires that the column names are the same in the objects that we are comparing.
We can use the rename() function from dplyr to do this. We list the value that we want to change to first. We find “food” more intuitive so we are going to change “rei_name” to “food” for the diet_data and the sep_age_diet_data:
# A tibble: 1 x 1
food
<chr>
1 fiberGreat, now we know that the fiber value appears to be different between the two.
Checking our original files we can see that the British spelling “fibre” is used in the table from the article (that we used to create guidelines), in contrast to the the American spelling “fiber” used in the CSV files.
Let’s stick with the American spelling, so we will replace "fibre" in the guideline tibble.
guidelines %<>%
mutate_at(vars(food),
~ str_replace(
string = .,
pattern = "fibre",
replacement = "fiber"))
guidelines %>%
filter(food == "fiber")# A tibble: 1 x 6
direction food lower optimal upper unit
<chr> <chr> <dbl> <dbl> <dbl> <chr>
1 low fiber 19 24 28 g Now let’s check again to see that our food values match between the guidelines and the observed consumption data tibbles.
# A tibble: 0 x 1
# … with 1 variable: food <chr># A tibble: 0 x 1
# … with 1 variable: food <chr>Great! There are no differences :)
Now we can put our guideline data together with the diet_data and the sep_age_diet_data.
Remember that the food data in our guidelines tibble is not necessarily in the same order as that of the consumption data tibbles. Thus this could be a problem if we decided to expand the guidelines rows (to repeat for the number of fruit observations etc.) and add them to our observed consumption tibbles by binding them together by column.
In that case we could use the arrange() function of dplyr to sort the data alphabetically.
However, we will instead use a joining function of dplyr. These functions combine the data together based on common values and don’t require the rows to be in the same order. There are a variety of options.
In our case we would like to retain all of the values of diet_data and sep_age_diet_data. We would like to add new columns of values to these tibbles that correspond to the guideline information about amounts of consumption for each food type in the guidelines tibble. We shouldn’t have any values of food in guidelines that don’t match, so we will not get any NA values. Therefore, in our case any of the mutating join functions should result in the same output.
It’s important to check if we have any overlapping variable names before we join the data. Otherwise, these columns will either be used to identify which rows to join, or new copies of the columns, with a default name to distinguish the columns of one data set from those of the other, will be created. We can use the base R function names() and the intersect() function of the dplyr package to identify which column names are common to our two data sets.
[1] "food" "upper" "lower" "unit" So it looks like the "upper" , "lower" and "unit" variable names are overlapping. Therefore, to distinguish the names later we will rename the guideline "upper" , "lower" and "unit" variables.
We will again use the rename function from the dplyr package. We can list multiple variables to rename and separate each with a comma. We need to list the new names first.
guidelines %<>%
rename(upper_optimal = upper,
lower_optimal = lower,
unit_optimal = unit)
guidelines# A tibble: 15 x 6
direction food lower_optimal optimal upper_optimal unit_optimal
<chr> <chr> <dbl> <dbl> <dbl> <chr>
1 low fruits 200 250 300 g
2 low vegetables 290 360 430 g
3 low legumes 50 60 70 g
4 low whole grains 100 125 150 g
5 low nuts and seeds 16 21 25 g
6 low milk 350 435 520 g
7 high red meat 18 23 27 g
8 high processed meat 0 2 4 g
9 high sugar-sweetened b… 0 3 5 g
10 low fiber 19 24 28 g
11 low calcium 1 1.25 1.5 g
12 low seafood omega-3 f… 200 250 300 mg
13 low polyunsaturated f… 9 11 13 %
14 high trans fatty acids 0 0.5 1 %
15 high sodium 1 3 5 g It is also a good idea to check our units to make sure they are the same for both guidelines and the observed consumption tibbles(diet_and_guidelines and all_age_diet_and_guidelines).
Let’s take a look with the count() function of the dplyr package. We will also use the bind_cols() function of dplyr to put the data together so that we can see it easily.
dplyr::bind_cols(count(diet_data, unit, food),
count(sep_age_diet_data,unit,food),
count(guidelines, unit_optimal, food))# A tibble: 15 x 9
unit...1 food...2 n...3 unit...4 food...5 n...6 unit_optimal food...8 n...9
<chr> <chr> <int> <chr> <chr> <int> <chr> <chr> <int>
1 %energy/… polyunsa… 392 %energy… polyuns… 5880 % polyuns… 1
2 %energy/… trans fa… 392 %energy… trans f… 5880 % trans f… 1
3 g/day calcium 392 g/day calcium 5880 g calcium 1
4 g/day fiber 392 g/day fiber 5880 g fiber 1
5 g/day fruits 392 g/day fruits 5880 g fruits 1
6 g/day legumes 392 g/day legumes 5880 g legumes 1
7 g/day milk 392 g/day milk 5880 g milk 1
8 g/day nuts and… 392 g/day nuts an… 5880 g nuts an… 1
9 g/day processe… 392 g/day process… 5880 g process… 1
10 g/day red meat 392 g/day red meat 5880 g red meat 1
11 g/day seafood … 392 g/day seafood… 5880 g sodium 1
12 g/day sodium 392 g/day sodium 5880 g sugar-s… 1
13 g/day sugar-sw… 392 g/day sugar-s… 5880 g vegetab… 1
14 g/day vegetabl… 392 g/day vegetab… 5880 g whole g… 1
15 g/day whole gr… 392 g/day whole g… 5880 mg seafood… 1We can see that the only potential issue is the seafood omega-3 fatty acids data which is in g/day for the observed data(diet_data and all_age_diet_and_guidelines), but in mg/day in the guidelines data.
We can account for this by dividing the guidelines seafood omega-3 fatty acids data by 1000 to convert it to grams from milligrams.
To do this we will use the if_else() function in the dplyr package. This allows us to specify a condition (in this case if the unit is "mg"), as well as values if this is condition is met (true), or if the condition is not met (false).
In the following we mutate the values in each of the guideline numeric columns (lower, optimal and upper) one at a time. When we refer to lower for example we refer to the values in the column/variable. So if the condition is not met, then the original value is retained. We will also replace "mg" with "g" after everything is converted to grams.
# Another possible way with dplyr::case_when():
# guidelines %<>%
# mutate(lower_optimal =case_when(
# unit_optimal == "mg" ~ lower_optimal/1000,
# unit_optimal != "mg" ~ lower_optimal))
guidelines %<>% mutate(lower_optimal = dplyr::if_else(
condition = unit_optimal == "mg",
true = lower_optimal/1000,
false = lower_optimal))
guidelines %<>% mutate(optimal = if_else(
condition = unit_optimal == "mg",
true = optimal/1000,
false = optimal))
guidelines %<>% mutate(upper_optimal = if_else(
condition = unit_optimal == "mg",
true = upper_optimal/1000,
false = upper_optimal))
guidelines %<>% mutate(unit_optimal = if_else(
condition = unit_optimal == "mg",
true = "g",
false = unit_optimal))
#Or could use this:
# guidelines %<>%
# mutate_at(vars(unit_optimal),
# ~str_replace(
# string = .,
# pattern = "mg",
# replacement = "g"))
guidelines# A tibble: 15 x 6
direction food lower_optimal optimal upper_optimal unit_optimal
<chr> <chr> <dbl> <dbl> <dbl> <chr>
1 low fruits 200 250 300 g
2 low vegetables 290 360 430 g
3 low legumes 50 60 70 g
4 low whole grains 100 125 150 g
5 low nuts and seeds 16 21 25 g
6 low milk 350 435 520 g
7 high red meat 18 23 27 g
8 high processed meat 0 2 4 g
9 high sugar-sweetened b… 0 3 5 g
10 low fiber 19 24 28 g
11 low calcium 1 1.25 1.5 g
12 low seafood omega-3 f… 0.2 0.25 0.3 g
13 low polyunsaturated f… 9 11 13 %
14 high trans fatty acids 0 0.5 1 %
15 high sodium 1 3 5 g In contrast we could have changed or mutated the values for lower_optimal, optimal, upper_optimal all at once like this using the funs() argument in mutate_at() of dplyr.
guidelines[str_which(string = guidelines[["food"]],
pattern = "seafood omega-3 fatty acids"),] <- guidelines %>%
filter(food == "seafood omega-3 fatty acids") %>%
mutate_at(vars(lower_optimal:upper_optimal), funs(./1000))Now we are ready to join the data!
Again, we would like to add new columns of values to diet_data and all_age_diet_and_guidelines that correspond to the guideline information about amounts of consumption for each food type in the guidelines tibble. So we will join the data based on the food variable values. We will use the full_join() function of the dplyr package.
diet_and_guidelines <- diet_data %>%
dplyr::full_join(guidelines, by = "food" )
all_age_diet_and_guidelines <- sep_age_diet_data %>%
full_join(guidelines, by = "food" )
glimpse(diet_and_guidelines)Rows: 5,880
Columns: 16
$ year_id <dbl> 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, …
$ location_name <chr> "Global", "Global", "Global", "Global", "Global", "Glo…
$ rei_id <dbl> 111, 111, 112, 112, 113, 113, 114, 114, 115, 115, 116,…
$ food <chr> "fruits", "fruits", "vegetables", "vegetables", "whole…
$ age_group_name <chr> "All Available Ages", "All Available Ages", "All Avail…
$ sex <chr> "Male", "Female", "Male", "Female", "Male", "Female", …
$ parameter <chr> "continuous", "continuous", "continuous", "continuous"…
$ mean <dbl> 8.703202e+01, 9.953182e+01, 1.975187e+02, 1.832338e+02…
$ upper <dbl> 8.988734e+01, 1.023450e+02, 2.083679e+02, 1.921103e+02…
$ lower <dbl> 8.452347e+01, 9.672140e+01, 1.887154e+02, 1.754019e+02…
$ unit <chr> "g/day", "g/day", "g/day", "g/day", "g/day", "g/day", …
$ direction <chr> "low", "low", "low", "low", "low", "low", "low", "low"…
$ lower_optimal <dbl> 200.0, 200.0, 290.0, 290.0, 100.0, 100.0, 16.0, 16.0, …
$ optimal <dbl> 250.00, 250.00, 360.00, 360.00, 125.00, 125.00, 21.00,…
$ upper_optimal <dbl> 300.0, 300.0, 430.0, 430.0, 150.0, 150.0, 25.0, 25.0, …
$ unit_optimal <chr> "g", "g", "g", "g", "g", "g", "g", "g", "g", "g", "g",…Rows: 88,200
Columns: 16
$ year_id <dbl> 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, …
$ location_name <chr> "Global", "Global", "Global", "Global", "Global", "Glo…
$ food <chr> "fruits", "fruits", "fruits", "fruits", "fruits", "fru…
$ age_group_id <dbl> 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 30, 31, 32…
$ age_group_name <chr> "25 to 29", "30 to 34", "35 to 39", "40 to 44", "45 to…
$ sex <chr> "Male", "Male", "Male", "Male", "Male", "Male", "Male"…
$ parameter <chr> "continuous", "continuous", "continuous", "continuous"…
$ mean <dbl> 68.54567, 72.62209, 76.73157, 81.03910, 88.79202, 95.9…
$ upper <dbl> 75.16806, 80.00433, 84.73205, 89.52747, 100.94301, 107…
$ lower <dbl> 63.17081, 66.78018, 71.04691, 73.82734, 80.40971, 86.4…
$ unit <chr> "g/day", "g/day", "g/day", "g/day", "g/day", "g/day", …
$ direction <chr> "low", "low", "low", "low", "low", "low", "low", "low"…
$ lower_optimal <dbl> 200, 200, 200, 200, 200, 200, 200, 200, 200, 200, 200,…
$ optimal <dbl> 250, 250, 250, 250, 250, 250, 250, 250, 250, 250, 250,…
$ upper_optimal <dbl> 300, 300, 300, 300, 300, 300, 300, 300, 300, 300, 300,…
$ unit_optimal <chr> "g", "g", "g", "g", "g", "g", "g", "g", "g", "g", "g",…It’s always a good idea to check that the values are what you expect after merging.
# A tibble: 15 x 3
food optimal n
<chr> <dbl> <int>
1 calcium 1.25 392
2 fiber 24 392
3 fruits 250 392
4 legumes 60 392
5 milk 435 392
6 nuts and seeds 21 392
7 polyunsaturated fatty acids 11 392
8 processed meat 2 392
9 red meat 23 392
10 seafood omega-3 fatty acids 0.25 392
11 sodium 3 392
12 sugar-sweetened beverages 3 392
13 trans fatty acids 0.5 392
14 vegetables 360 392
15 whole grains 125 392# A tibble: 15 x 3
food optimal n
<chr> <dbl> <int>
1 calcium 1.25 5880
2 fiber 24 5880
3 fruits 250 5880
4 legumes 60 5880
5 milk 435 5880
6 nuts and seeds 21 5880
7 polyunsaturated fatty acids 11 5880
8 processed meat 2 5880
9 red meat 23 5880
10 seafood omega-3 fatty acids 0.25 5880
11 sodium 3 5880
12 sugar-sweetened beverages 3 5880
13 trans fatty acids 0.5 5880
14 vegetables 360 5880
15 whole grains 125 5880# A tibble: 15 x 6
direction food lower_optimal optimal upper_optimal unit_optimal
<chr> <chr> <dbl> <dbl> <dbl> <chr>
1 low calcium 1 1.25 1.5 g
2 low fiber 19 24 28 g
3 low fruits 200 250 300 g
4 low legumes 50 60 70 g
5 low milk 350 435 520 g
6 low nuts and seeds 16 21 25 g
7 low polyunsaturated f… 9 11 13 %
8 high processed meat 0 2 4 g
9 high red meat 18 23 27 g
10 low seafood omega-3 f… 0.2 0.25 0.3 g
11 high sodium 1 3 5 g
12 high sugar-sweetened b… 0 3 5 g
13 high trans fatty acids 0 0.5 1 %
14 low vegetables 290 360 430 g
15 low whole grains 100 125 150 g Looks good!
Recall that our aim is to compare the consumption rates of these dietary factors by different groups of people, and ideally, to facilitate cross-factor comparisons, we want to consider consumption rates relative to the optimal guidelines.
To do this, let’s calculate values of consumption that are relative to the suggested guidelines.
There are a few approaches we could take. One is to calculate a "percentage of optimal consumption" using the mean value for each observed factor relative to its optimal value. To do this we will use the mutate() function of the dplyrpackage. This will create a new variable called Relative_Percent that will be equal to the ratio of the mean value and the optimal value, multiplied by 100, to create a percentage relative to the optimal amount suggested.
diet_and_guidelines %<>%
mutate(Relative_Percent = (mean/optimal)*100)
all_age_diet_and_guidelines %<>%
mutate(Relative_Percent = (mean/optimal)*100)Another option is to incorporate the range of optimal intakes and the direction that is associated with health risk. If the direction of risk is high and the consumption was greater than the optimal mean value, than the percentage is calculated based on the upper_optimal value, while if the direction of risk is low and the consumption is less than the optimal mean value, then the percentage is calculated based on the lower_optimal value. We will use the case_when() function of the dplyr package to do this. This allows us to specify values (indicated on the right side of the ~symbol) based on specific conditions (indicated on the left side of the ~ symbol). We can specify multiple conditions using the & symbol.
diet_and_guidelines %<>%
mutate(range_percent = case_when(
direction == "high" ~ (mean/upper_optimal)*100,
direction == "low" ~ (mean/lower_optimal)*100))
all_age_diet_and_guidelines %<>%
mutate(range_percent = case_when(
direction == "high" ~ (mean/upper_optimal)*100,
direction == "low" ~ (mean/lower_optimal)*100))
diet_and_guidelines %<>%
mutate(percent_over_under = case_when(
direction == "high" & mean > upper_optimal ~
((mean-upper_optimal)/upper_optimal)*100,
direction == "high" & mean <= upper_optimal ~ 0,
direction == "low" & mean >= lower_optimal ~ 0,
direction == "low" & mean < lower_optimal ~
((lower_optimal-mean)/lower_optimal)*-100))
all_age_diet_and_guidelines %<>%
mutate(percent_over_under = case_when(
direction == "high" & mean > upper_optimal ~
((mean-upper_optimal)/upper_optimal)*100,
direction == "high" & mean <= upper_optimal ~ 0,
direction == "low" & mean >= lower_optimal ~ 0,
direction == "low" & mean < lower_optimal ~
((lower_optimal-mean)/lower_optimal)*-100))Another option is to create a binary outcome indicating whether optimal consumption was achieved or not.
diet_and_guidelines %<>%
mutate(opt_achieved = if_else(
condition = direction == "low" & mean > lower_optimal |
direction == "high" & mean < upper_optimal,
true = "Yes",
false = "No"))
all_age_diet_and_guidelines %<>%
mutate(opt_achieved = if_else(
condition = direction == "low" & mean > lower_optimal |
direction == "high" & mean < upper_optimal,
true = "Yes",
false = "No"))
glimpse(diet_and_guidelines)Rows: 5,880
Columns: 20
$ year_id <dbl> 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 20…
$ location_name <chr> "Global", "Global", "Global", "Global", "Global", …
$ rei_id <dbl> 111, 111, 112, 112, 113, 113, 114, 114, 115, 115, …
$ food <chr> "fruits", "fruits", "vegetables", "vegetables", "w…
$ age_group_name <chr> "All Available Ages", "All Available Ages", "All A…
$ sex <chr> "Male", "Female", "Male", "Female", "Male", "Femal…
$ parameter <chr> "continuous", "continuous", "continuous", "continu…
$ mean <dbl> 8.703202e+01, 9.953182e+01, 1.975187e+02, 1.832338…
$ upper <dbl> 8.988734e+01, 1.023450e+02, 2.083679e+02, 1.921103…
$ lower <dbl> 8.452347e+01, 9.672140e+01, 1.887154e+02, 1.754019…
$ unit <chr> "g/day", "g/day", "g/day", "g/day", "g/day", "g/da…
$ direction <chr> "low", "low", "low", "low", "low", "low", "low", "…
$ lower_optimal <dbl> 200.0, 200.0, 290.0, 290.0, 100.0, 100.0, 16.0, 16…
$ optimal <dbl> 250.00, 250.00, 360.00, 360.00, 125.00, 125.00, 21…
$ upper_optimal <dbl> 300.0, 300.0, 430.0, 430.0, 150.0, 150.0, 25.0, 25…
$ unit_optimal <chr> "g", "g", "g", "g", "g", "g", "g", "g", "g", "g", …
$ Relative_Percent <dbl> 34.8128083, 39.8127275, 54.8663179, 50.8982797, 23…
$ range_percent <dbl> 43.5160103, 49.7659094, 68.1099118, 63.1840714, 29…
$ percent_over_under <dbl> -56.483990, -50.234091, -31.890088, -36.815929, -7…
$ opt_achieved <chr> "No", "No", "No", "No", "No", "No", "No", "No", "N…Rows: 88,200
Columns: 20
$ year_id <dbl> 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 20…
$ location_name <chr> "Global", "Global", "Global", "Global", "Global", …
$ food <chr> "fruits", "fruits", "fruits", "fruits", "fruits", …
$ age_group_id <dbl> 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 30, 31…
$ age_group_name <chr> "25 to 29", "30 to 34", "35 to 39", "40 to 44", "4…
$ sex <chr> "Male", "Male", "Male", "Male", "Male", "Male", "M…
$ parameter <chr> "continuous", "continuous", "continuous", "continu…
$ mean <dbl> 68.54567, 72.62209, 76.73157, 81.03910, 88.79202, …
$ upper <dbl> 75.16806, 80.00433, 84.73205, 89.52747, 100.94301,…
$ lower <dbl> 63.17081, 66.78018, 71.04691, 73.82734, 80.40971, …
$ unit <chr> "g/day", "g/day", "g/day", "g/day", "g/day", "g/da…
$ direction <chr> "low", "low", "low", "low", "low", "low", "low", "…
$ lower_optimal <dbl> 200, 200, 200, 200, 200, 200, 200, 200, 200, 200, …
$ optimal <dbl> 250, 250, 250, 250, 250, 250, 250, 250, 250, 250, …
$ upper_optimal <dbl> 300, 300, 300, 300, 300, 300, 300, 300, 300, 300, …
$ unit_optimal <chr> "g", "g", "g", "g", "g", "g", "g", "g", "g", "g", …
$ Relative_Percent <dbl> 27.41827, 29.04883, 30.69263, 32.41564, 35.51681, …
$ range_percent <dbl> 34.27284, 36.31104, 38.36578, 40.51955, 44.39601, …
$ percent_over_under <dbl> -65.72716, -63.68896, -61.63422, -59.48045, -55.60…
$ opt_achieved <chr> "No", "No", "No", "No", "No", "No", "No", "No", "N…One last thing that can be useful with data wrangling is to reshape the data into what is called the long format. This is very useful for creating visualizations with a powerful and flexible package called ggplot2.
To coerce an object into long format, we create more rows and fewer columns. For a more information about this, please see this case study.
We would like to put together the different types of percentages of the optimal intake that we just calculated.
To get our data in long format we can use the pivot_longer() function of the dplyr package. We will also show how this would be done with the older version of this function, called gather().
For pivot_longer(), we will list the columns that we want to come together into the longer format using the cols argument. For gather() we would simply list the variables that we wish to consolidate. The names_to argument indicates the name of the variable that will include the character information about the values that we are consolidating, i.e., this variable contains the names of the columns that we are bringing together. This is equivalent to the key argument in gather(). The values_to is the name of the column that will contain the values of the columns we are consolidating. This is equivalent to the value argument in gather(). We can use contains() of the tidyr package to look at the variables with names that contain "percent" .
We would get an identical output from the methods. We can check that with setequal().
diet_and_guidelines_long <- diet_and_guidelines %>%
pivot_longer(cols = contains("percent"),
names_to = "percent_type",
values_to = "percent")
diet_and_guidelines_long2 <- diet_and_guidelines %>%
gather(contains("percent"),
key = percent_type,
value = percent)
setequal(diet_and_guidelines_long, diet_and_guidelines_long2)[1] TRUELet’s do the same for the age separated data.
all_age_diet_and_guidelines_long <- all_age_diet_and_guidelines %>%
pivot_longer(cols = contains("percent"),
names_to = "percent_type",
values_to = "percent")We now have the main variables and data formats that we need to proceed with the next steps of our analysis, including data exploration and eventually, modeling.
Let’s start by taking a look at the percent of consumption, across all dietary factors. Again we will use the base R summary() function:
Relative_Percent
Min. : 0.02
1st Qu.: 11.20
Median : 34.71
Mean : 187.86
3rd Qu.: 73.12
Max. :7693.68 Wow! Some of the values are nearly zero, suggesting that some people are consuming basically zero percent of what is suggested for optimal health. On the other hand, for some dietary factors people are consuming over 7,000 percent what is suggested!
This is why it is important to look at the direction of consumption that could be harmful. For example if there is a population that consumes large amounts of vegetables this could be a good thing, but if there is a population consuming large amounts of sodium this would be a bad thing.
Let’s take a look to see what dietary factors are at the extremes by arranging the data using the arrange() function of the dplyr package. We can arrange by smallest to largest using the default and we can arrange largest to smallest using the minus sign -.
Rows: 5,880
Columns: 20
$ year_id <dbl> 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 20…
$ location_name <chr> "Costa Rica", "Colombia", "Panama", "Venezuela", "…
$ rei_id <dbl> 118, 118, 118, 118, 118, 118, 118, 118, 118, 118, …
$ food <chr> "sugar-sweetened beverages", "sugar-sweetened beve…
$ age_group_name <chr> "All Available Ages", "All Available Ages", "All A…
$ sex <chr> "Male", "Male", "Male", "Male", "Male", "Male", "M…
$ parameter <chr> "continuous", "continuous", "continuous", "continu…
$ mean <dbl> 230.8105, 201.7180, 194.4116, 193.9048, 188.4661, …
$ upper <dbl> 247.9342, 216.9260, 208.8040, 208.8702, 203.3799, …
$ lower <dbl> 215.5919, 189.4812, 181.7150, 180.7209, 174.5476, …
$ unit <chr> "g/day", "g/day", "g/day", "g/day", "g/day", "g/da…
$ direction <chr> "high", "high", "high", "high", "high", "high", "h…
$ lower_optimal <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ optimal <dbl> 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,…
$ upper_optimal <dbl> 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,…
$ unit_optimal <chr> "g", "g", "g", "g", "g", "g", "g", "g", "g", "g", …
$ Relative_Percent <dbl> 7693.684, 6723.934, 6480.386, 6463.492, 6282.204, …
$ range_percent <dbl> 4616.211, 4034.361, 3888.232, 3878.095, 3769.322, …
$ percent_over_under <dbl> 4516.211, 3934.361, 3788.232, 3778.095, 3669.322, …
$ opt_achieved <chr> "No", "No", "No", "No", "No", "No", "No", "No", "N…OK, so it looks like sugar-sweetened beverages are really over-consumed in some parts of the world!
Recall from the supplementary table from the article that over-consumption of sugar-sweetened beverages is associated with both Diabetes mellitus type 2 and Ischemic heart disease. This article discusses some of the controversy over the potential health risks associated with high consumption of sugar.
It still looks quite bad if we look at the other calculated percentage values.
Relative_Percent range_percent percent_over_under
Min. : 0.02 Min. : 0.025 Min. : -99.97
1st Qu.: 11.20 1st Qu.: 13.263 1st Qu.: -77.60
Median : 34.71 Median : 38.941 Median : -42.20
Mean : 187.86 Mean : 127.272 Mean : 39.49
3rd Qu.: 73.12 3rd Qu.: 72.414 3rd Qu.: 0.00
Max. :7693.68 Max. :4616.211 Max. :4516.21 So some places are still consuming 4,000 percent more than the upper range of the suggested optimal intake.
Let’s take a look at global levels:
# A tibble: 2 x 20
year_id location_name rei_id food age_group_name sex parameter mean upper
<dbl> <chr> <dbl> <chr> <chr> <chr> <chr> <dbl> <dbl>
1 2017 Global 118 suga… All Available… Male continuo… 65.5 71.0
2 2017 Global 118 suga… All Available… Fema… continuo… 47.7 51.4
# … with 11 more variables: lower <dbl>, unit <chr>, direction <chr>,
# lower_optimal <dbl>, optimal <dbl>, upper_optimal <dbl>,
# unit_optimal <chr>, Relative_Percent <dbl>, range_percent <dbl>,
# percent_over_under <dbl>, opt_achieved <chr>For those who are less familiar with the metric system where grams are equivalent to milliliters, it may be useful to realize how many fluid ounces the max amount of consumption per day (~248g for the upper value for Costa Rica) actually is.
There are 0.35247 ounces in one gram.
[1] 87.38937OK, so the top consumers are drinking about 87 fluid ounces per day. Since there are 12 ounces in a single can of soda, this is about 7.25 sodas per day. Globally on average, males are drinking around 1.924 sodas worth of sweetened beverages, while females are drinking about 1.401.
Let’s take a look at what is under-consumed:
Rows: 5,880
Columns: 20
$ year_id <dbl> 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 20…
$ location_name <chr> "Chad", "Chad", "Mali", "Mali", "Burkina Faso", "B…
$ rei_id <dbl> 122, 122, 122, 122, 122, 122, 122, 122, 122, 122, …
$ food <chr> "polyunsaturated fatty acids", "polyunsaturated fa…
$ age_group_name <chr> "All Available Ages", "All Available Ages", "All A…
$ sex <chr> "Female", "Male", "Female", "Male", "Female", "Mal…
$ parameter <chr> "continuous", "continuous", "continuous", "continu…
$ mean <dbl> 0.002227074, 0.002295916, 0.002301266, 0.002373331…
$ upper <dbl> 0.002383410, 0.002439931, 0.002461209, 0.002520039…
$ lower <dbl> 0.002082132, 0.002161922, 0.002152212, 0.002229945…
$ unit <chr> "%energy/day", "%energy/day", "%energy/day", "%ene…
$ direction <chr> "low", "low", "low", "low", "low", "low", "low", "…
$ lower_optimal <dbl> 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,…
$ optimal <dbl> 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11…
$ upper_optimal <dbl> 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13…
$ unit_optimal <chr> "%", "%", "%", "%", "%", "%", "%", "%", "%", "%", …
$ Relative_Percent <dbl> 0.02024613, 0.02087196, 0.02092060, 0.02157574, 0.…
$ range_percent <dbl> 0.02474527, 0.02551018, 0.02556962, 0.02637034, 0.…
$ percent_over_under <dbl> -99.97525, -99.97449, -99.97443, -99.97363, -99.96…
$ opt_achieved <chr> "No", "No", "No", "No", "No", "No", "No", "No", "N…On the other hand, it looks like some places are consuming almost no polyunsaturated fatty acids. These are fats that found in plant-based sources like seeds and nuts. According to an article about polyunsaturated fatty acids and its influence on health:
Coronary heart disease (CHD) is the leading cause of death worldwide … The types of dietary fats consumed play an important role in CHD risk, representing key modifiable risk factors…In particular, higher intakes of trans fat (TFA) and of saturated fat (SFA) replacing ω‐6 (n‐6) polyunsaturated fat (PUFA) are associated with increased CHD… whereas higher intake of PUFA replacing either SFA or carbohydrate is associated with lower risk.
Let’s get an idea about how countries compare in terms of how many of the dietary factors are consumed at the optimal level (the opt_achieved variable).
# A tibble: 2 x 2
opt_achieved n
<chr> <int>
1 No 4360
2 Yes 1520Looks like overall, only 34.86% of dietary factors for all tested populations were at optimal levels.
Let’s get an idea about how countries compare on this metric.
diet_and_guidelines %>%
count(opt_achieved, location_name) %>%
filter(opt_achieved == "Yes") %>%
arrange(-n) %>%
# this allows us to show the full output
print(n = 1e3)# A tibble: 196 x 3
opt_achieved location_name n
<chr> <chr> <int>
1 Yes Qatar 13
2 Yes Rwanda 13
3 Yes Turkey 13
4 Yes Dominican Republic 12
5 Yes Egypt 12
6 Yes Malawi 12
7 Yes Benin 11
8 Yes Burkina Faso 11
9 Yes Cameroon 11
10 Yes Cuba 11
11 Yes Ethiopia 11
12 Yes Ghana 11
13 Yes Lesotho 11
14 Yes Mali 11
15 Yes Nigeria 11
16 Yes Senegal 11
17 Yes Togo 11
18 Yes Tunisia 11
19 Yes Angola 10
20 Yes Azerbaijan 10
21 Yes Bahrain 10
22 Yes Barbados 10
23 Yes Bermuda 10
24 Yes Chad 10
25 Yes Comoros 10
26 Yes Cote d'Ivoire 10
27 Yes El Salvador 10
28 Yes Guatemala 10
29 Yes Haiti 10
30 Yes India 10
31 Yes Iran 10
32 Yes Kenya 10
33 Yes Lebanon 10
34 Yes Mauritania 10
35 Yes Mauritius 10
36 Yes Morocco 10
37 Yes Mozambique 10
38 Yes Myanmar 10
39 Yes Nepal 10
40 Yes Nicaragua 10
41 Yes Niger 10
42 Yes North Korea 10
43 Yes Sao Tome and Principe 10
44 Yes Sierra Leone 10
45 Yes South Sudan 10
46 Yes Tanzania 10
47 Yes Timor-Leste 10
48 Yes Uganda 10
49 Yes United Arab Emirates 10
50 Yes Zambia 10
51 Yes Armenia 9
52 Yes Bosnia and Herzegovina 9
53 Yes Burundi 9
54 Yes Costa Rica 9
55 Yes Equatorial Guinea 9
56 Yes Eritrea 9
57 Yes Fiji 9
58 Yes Honduras 9
59 Yes Iraq 9
60 Yes Kuwait 9
61 Yes Oman 9
62 Yes Papua New Guinea 9
63 Yes Solomon Islands 9
64 Yes Tajikistan 9
65 Yes Trinidad and Tobago 9
66 Yes Uzbekistan 9
67 Yes Virgin Islands, U.S. 9
68 Yes Afghanistan 8
69 Yes Albania 8
70 Yes Algeria 8
71 Yes American Samoa 8
72 Yes Antigua and Barbuda 8
73 Yes Bangladesh 8
74 Yes Belize 8
75 Yes Bhutan 8
76 Yes Botswana 8
77 Yes Brazil 8
78 Yes Cambodia 8
79 Yes Cape Verde 8
80 Yes Congo 8
81 Yes Democratic Republic of the Congo 8
82 Yes Djibouti 8
83 Yes Dominica 8
84 Yes Federated States of Micronesia 8
85 Yes Gabon 8
86 Yes Georgia 8
87 Yes Grenada 8
88 Yes Guam 8
89 Yes Guinea 8
90 Yes Guinea-Bissau 8
91 Yes Guyana 8
92 Yes Indonesia 8
93 Yes Israel 8
94 Yes Jamaica 8
95 Yes Jordan 8
96 Yes Kazakhstan 8
97 Yes Kiribati 8
98 Yes Kyrgyzstan 8
99 Yes Laos 8
100 Yes Liberia 8
101 Yes Libya 8
102 Yes Macedonia 8
103 Yes Madagascar 8
104 Yes Malaysia 8
105 Yes Maldives 8
106 Yes Marshall Islands 8
107 Yes Mexico 8
108 Yes Moldova 8
109 Yes Namibia 8
110 Yes Northern Mariana Islands 8
111 Yes Pakistan 8
112 Yes Palestine 8
113 Yes Peru 8
114 Yes Philippines 8
115 Yes Saint Lucia 8
116 Yes Saint Vincent and the Grenadines 8
117 Yes Samoa 8
118 Yes Saudi Arabia 8
119 Yes Seychelles 8
120 Yes Somalia 8
121 Yes Sri Lanka 8
122 Yes Sudan 8
123 Yes Suriname 8
124 Yes Swaziland 8
125 Yes Syria 8
126 Yes Taiwan 8
127 Yes Thailand 8
128 Yes The Bahamas 8
129 Yes The Gambia 8
130 Yes Tonga 8
131 Yes Turkmenistan 8
132 Yes Vanuatu 8
133 Yes Yemen 8
134 Yes Zimbabwe 8
135 Yes Bolivia 7
136 Yes Central African Republic 7
137 Yes Luxembourg 7
138 Yes Montenegro 7
139 Yes Panama 7
140 Yes Paraguay 7
141 Yes Puerto Rico 7
142 Yes South Africa 7
143 Yes Ukraine 7
144 Yes Venezuela 7
145 Yes Vietnam 7
146 Yes Canada 6
147 Yes Colombia 6
148 Yes Greece 6
149 Yes Japan 6
150 Yes Malta 6
151 Yes Mongolia 6
152 Yes Portugal 6
153 Yes Romania 6
154 Yes Serbia 6
155 Yes Argentina 5
156 Yes Belarus 5
157 Yes Bulgaria 5
158 Yes Croatia 5
159 Yes Ecuador 5
160 Yes Finland 5
161 Yes Global 5
162 Yes Iceland 5
163 Yes Netherlands 5
164 Yes Russian Federation 5
165 Yes Sweden 5
166 Yes Andorra 4
167 Yes Australia 4
168 Yes Austria 4
169 Yes Belgium 4
170 Yes Brunei 4
171 Yes Chile 4
172 Yes China 4
173 Yes Cyprus 4
174 Yes Denmark 4
175 Yes Estonia 4
176 Yes France 4
177 Yes Germany 4
178 Yes Ireland 4
179 Yes Italy 4
180 Yes Latvia 4
181 Yes Lithuania 4
182 Yes New Zealand 4
183 Yes Norway 4
184 Yes Poland 4
185 Yes Singapore 4
186 Yes South Korea 4
187 Yes Spain 4
188 Yes Switzerland 4
189 Yes United Kingdom 4
190 Yes Uruguay 4
191 Yes Czech Republic 3
192 Yes Greenland 3
193 Yes Hungary 3
194 Yes Slovakia 3
195 Yes Slovenia 3
196 Yes United States 3It looks as though on average the populations (both male and female separately) in Qatar, Rwanda, and Turkey consumed the optimal level of intake for the largest number of dietary factors (13 out of 30 (for the 15 dietary factors for males and females)).
In contrast, the Czech Republic, Greenland, Hungary, Slovakia, Slovenia, and the United States had the poorest consumption rates (27 out of 30 were not at optimal levels).
diet_and_guidelines %>%
count(opt_achieved, location_name) %>%
filter(opt_achieved == "No") %>%
arrange(-n) %>%
#to show full output
print(n = 1e3)# A tibble: 196 x 3
opt_achieved location_name n
<chr> <chr> <int>
1 No Czech Republic 27
2 No Greenland 27
3 No Hungary 27
4 No Slovakia 27
5 No Slovenia 27
6 No United States 27
7 No Andorra 26
8 No Australia 26
9 No Austria 26
10 No Belgium 26
11 No Brunei 26
12 No Chile 26
13 No China 26
14 No Cyprus 26
15 No Denmark 26
16 No Estonia 26
17 No France 26
18 No Germany 26
19 No Ireland 26
20 No Italy 26
21 No Latvia 26
22 No Lithuania 26
23 No New Zealand 26
24 No Norway 26
25 No Poland 26
26 No Singapore 26
27 No South Korea 26
28 No Spain 26
29 No Switzerland 26
30 No United Kingdom 26
31 No Uruguay 26
32 No Argentina 25
33 No Belarus 25
34 No Bulgaria 25
35 No Croatia 25
36 No Ecuador 25
37 No Finland 25
38 No Global 25
39 No Iceland 25
40 No Netherlands 25
41 No Russian Federation 25
42 No Sweden 25
43 No Canada 24
44 No Colombia 24
45 No Greece 24
46 No Japan 24
47 No Malta 24
48 No Mongolia 24
49 No Portugal 24
50 No Romania 24
51 No Serbia 24
52 No Bolivia 23
53 No Central African Republic 23
54 No Luxembourg 23
55 No Montenegro 23
56 No Panama 23
57 No Paraguay 23
58 No Puerto Rico 23
59 No South Africa 23
60 No Ukraine 23
61 No Venezuela 23
62 No Vietnam 23
63 No Afghanistan 22
64 No Albania 22
65 No Algeria 22
66 No American Samoa 22
67 No Antigua and Barbuda 22
68 No Bangladesh 22
69 No Belize 22
70 No Bhutan 22
71 No Botswana 22
72 No Brazil 22
73 No Cambodia 22
74 No Cape Verde 22
75 No Congo 22
76 No Democratic Republic of the Congo 22
77 No Djibouti 22
78 No Dominica 22
79 No Federated States of Micronesia 22
80 No Gabon 22
81 No Georgia 22
82 No Grenada 22
83 No Guam 22
84 No Guinea 22
85 No Guinea-Bissau 22
86 No Guyana 22
87 No Indonesia 22
88 No Israel 22
89 No Jamaica 22
90 No Jordan 22
91 No Kazakhstan 22
92 No Kiribati 22
93 No Kyrgyzstan 22
94 No Laos 22
95 No Liberia 22
96 No Libya 22
97 No Macedonia 22
98 No Madagascar 22
99 No Malaysia 22
100 No Maldives 22
101 No Marshall Islands 22
102 No Mexico 22
103 No Moldova 22
104 No Namibia 22
105 No Northern Mariana Islands 22
106 No Pakistan 22
107 No Palestine 22
108 No Peru 22
109 No Philippines 22
110 No Saint Lucia 22
111 No Saint Vincent and the Grenadines 22
112 No Samoa 22
113 No Saudi Arabia 22
114 No Seychelles 22
115 No Somalia 22
116 No Sri Lanka 22
117 No Sudan 22
118 No Suriname 22
119 No Swaziland 22
120 No Syria 22
121 No Taiwan 22
122 No Thailand 22
123 No The Bahamas 22
124 No The Gambia 22
125 No Tonga 22
126 No Turkmenistan 22
127 No Vanuatu 22
128 No Yemen 22
129 No Zimbabwe 22
130 No Armenia 21
131 No Bosnia and Herzegovina 21
132 No Burundi 21
133 No Costa Rica 21
134 No Equatorial Guinea 21
135 No Eritrea 21
136 No Fiji 21
137 No Honduras 21
138 No Iraq 21
139 No Kuwait 21
140 No Oman 21
141 No Papua New Guinea 21
142 No Solomon Islands 21
143 No Tajikistan 21
144 No Trinidad and Tobago 21
145 No Uzbekistan 21
146 No Virgin Islands, U.S. 21
147 No Angola 20
148 No Azerbaijan 20
149 No Bahrain 20
150 No Barbados 20
151 No Bermuda 20
152 No Chad 20
153 No Comoros 20
154 No Cote d'Ivoire 20
155 No El Salvador 20
156 No Guatemala 20
157 No Haiti 20
158 No India 20
159 No Iran 20
160 No Kenya 20
161 No Lebanon 20
162 No Mauritania 20
163 No Mauritius 20
164 No Morocco 20
165 No Mozambique 20
166 No Myanmar 20
167 No Nepal 20
168 No Nicaragua 20
169 No Niger 20
170 No North Korea 20
171 No Sao Tome and Principe 20
172 No Sierra Leone 20
173 No South Sudan 20
174 No Tanzania 20
175 No Timor-Leste 20
176 No Uganda 20
177 No United Arab Emirates 20
178 No Zambia 20
179 No Benin 19
180 No Burkina Faso 19
181 No Cameroon 19
182 No Cuba 19
183 No Ethiopia 19
184 No Ghana 19
185 No Lesotho 19
186 No Mali 19
187 No Nigeria 19
188 No Senegal 19
189 No Togo 19
190 No Tunisia 19
191 No Dominican Republic 18
192 No Egypt 18
193 No Malawi 18
194 No Qatar 17
195 No Rwanda 17
196 No Turkey 17Let’s look at the raw US data:
Rows: 30
Columns: 20
$ year_id <dbl> 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 20…
$ location_name <chr> "United States", "United States", "United States",…
$ rei_id <dbl> 111, 111, 112, 112, 113, 113, 114, 114, 115, 115, …
$ food <chr> "fruits", "fruits", "vegetables", "vegetables", "w…
$ age_group_name <chr> "All Available Ages", "All Available Ages", "All A…
$ sex <chr> "Male", "Female", "Male", "Female", "Male", "Femal…
$ parameter <chr> "continuous", "continuous", "continuous", "continu…
$ mean <dbl> 114.24773940, 129.11513300, 208.93230110, 192.6563…
$ upper <dbl> 121.37292480, 136.94611510, 217.80223830, 200.7148…
$ lower <dbl> 107.89222320, 122.33941970, 200.05582000, 184.5778…
$ unit <chr> "g/day", "g/day", "g/day", "g/day", "g/day", "g/da…
$ direction <chr> "low", "low", "low", "low", "low", "low", "low", "…
$ lower_optimal <dbl> 200.0, 200.0, 290.0, 290.0, 100.0, 100.0, 16.0, 16…
$ optimal <dbl> 250.00, 250.00, 360.00, 360.00, 125.00, 125.00, 21…
$ upper_optimal <dbl> 300.0, 300.0, 430.0, 430.0, 150.0, 150.0, 25.0, 25…
$ unit_optimal <chr> "g", "g", "g", "g", "g", "g", "g", "g", "g", "g", …
$ Relative_Percent <dbl> 45.699096, 51.646053, 58.036750, 53.515649, 23.121…
$ range_percent <dbl> 57.123870, 64.557566, 72.045621, 66.433220, 28.901…
$ percent_over_under <dbl> -42.876130, -35.442434, -27.954379, -33.566780, -7…
$ opt_achieved <chr> "No", "No", "No", "No", "No", "No", "No", "No", "N…Let’s see how males and females compare for achieving the optimal intake, across all countries:
# A tibble: 4 x 3
sex opt_achieved n
<chr> <chr> <int>
1 Female No 2171
2 Female Yes 769
3 Male No 2189
4 Male Yes 751Looks pretty similar, but it may be a bit better for females. We will evaluate this further below.
In order to assess what we have observed so far in a graphical way, we will make some data visualizations. One way we can do this is with the ggplot2 package. The ggplot2 package creates plots by building the plot components piece by piece, using "layers".
With ggplot2 we select what data we would like to plot using the first function (ggplot()) and then we add on additional layers of complexity (these layers can even involve different data). The aes() argument specifies what aspects of the data will be plotted where. The geom_* function specifies what type of plot to create (e.g. geom_histogram() creates a histogram). Notice in the following code how there is a plus sign between the ggplot() function and the geom_bar() function; this is how we combine different plot layers.
We will see later how we can add many layers to plots with ggplot2. For additional information on using ggplot2, see this case study.
Continuing with ggplot2 we will now create a different type of plot. This time we will create a series of boxplots. We will use the facet_wrap() function of ggplot2 to allow us to create many different plots simultaneously. In this case we can look at boxplots for the different dietary factors colored by sex. The scales argument when set to "free" means that each of the sequential plot created by the facet can have a different scale for the y axis, otherwise, by default they are constrained to the same scale. Since our dietary factors are measured on very different scales, we do not want this constraint here.
# we will create a new variable with food names with new lines
diet_and_guidelines %<>%
mutate(food_to_plot =
str_replace(
string = pull(diet_and_guidelines,food),
pattern = " ",
replacement = "\n"))
diet_and_guidelines %>%
ggplot(aes(y = Relative_Percent ,
x = sex,
color = sex)) +
geom_boxplot() +
facet_wrap(~ food_to_plot,
scales = "free",
#specifies the number of rows of subplots
nrow = 3,
#moves the food label to the right
strip.position = "right") +
#this changes the size of the font for the labels
theme(strip.text.y = element_text(size = 8),
axis.text.x = element_text(angle = 70,
hjust = 1))
If we just look at differences by sex for the specific dietary factors, males appear to potentially consume more of many of the factors, including possibly more sodium, fiber, calcium, red meat, and sugar-sweetened beverages than females. Females may consume more fruit.
Now we will take a look at the data that is separated by age groups.
First, recall that we have 15 different age groups starting from age 25 to 95 plus.
# A tibble: 15 x 2
age_group_name n
<chr> <int>
1 25 to 29 5880
2 30 to 34 5880
3 35 to 39 5880
4 40 to 44 5880
5 45 to 49 5880
6 50 to 54 5880
7 55 to 59 5880
8 60 to 64 5880
9 65 to 69 5880
10 70 to 74 5880
11 75 to 79 5880
12 80 to 84 5880
13 85 to 89 5880
14 90 to 94 5880
15 95 plus 5880sep_age_diet_data %>%
ggplot(aes(y = mean , x= age_group_name, col = sex)) +
geom_boxplot() +
facet_wrap(~food, scales = "free", nrow = 6) +
theme(axis.text.x = element_text(angle = 70, hjust = 1))
We can see from these plots that there appear to be age differences and gender differences for some of the different dietary factors. We will work to create clearer figures later on. However these initial figures have given us a better sense of the data that we are working with.
Recall what our main questions were:
Our main questions are:
We have some general sense about global trends for the risk-associated dietary factors, however we want to know more.
We are interested in how much the genders differ, how much the 15 different age groups differ, and how the 195 countries compare.
In order to make inferences about these comparisons, it is helpful to perform statistical tests. These tests can help us to determine the strength of the association between sex, age group, and country identity with relative consumption of the dietary factors of interest. One way to look at the strength of association is to use a statistical method called linear regression.
You may have already learned that one can compare two groups using a \(t\)-test. For more information on the \(t\)-test see this case study.
Perhaps you have heard about the ANOVA test, which can be useful for comparing more than two groups. ANOVA stands for “ANalysis Of VAriance”.
It turns out that both the \(t\)-test and ANOVA are equivalent to specialized types of regression. We will use each of these tests to investigate patterns of consumption for dietary factors that contribute to health risk and we will look at how we can obtain equivalent results with regression.
So what is regression? How can we use regression to compare our groups of interest and look at the relationship between group identity and consumption of dietary factors associated with health risk?
The statistical version of the term regression was coined in 1877 in this article about the relationship between hereditary traits and population averages. The author particularly focused on height and kinship or relatedness. The word itself means "to go back to a simpler state". It was noticed that individuals with parents who had an extreme trait, such as exceptional height, tended to have a height more similar to the average of the population than the extreme height of their parents. For example if parents were very tall, their children were likely to be a bit shorter than their parents and therefore closer to the population average. Thus the children regressed towards the mean or in the author’s words the offspring showed:
“a regression towards mediocrity”
See here for more information about this history.
When we think about this from a statistical standpoint, regression allows us to estimate or regress relationships between variables with a “simple” model. We do this by estimating the mean of an outcome, given a value of an input or predictor variable. This can be useful for predicting future values of the outcome based on the approximation of the real relationship between the variables within the model, or just for understanding how different variables are related to one another.
We will start by considering simple linear regression, where we have one predictor variable and one outcome variable. Using the ordinary least squares method we can identify a line that best fits the data by minimizing the sum of the squared vertical distances between each point and the line.
Fitting a line to the data like this allows us to create a formula for the line using an intercept and a slope, so that we can then estimate mean values of \(Y\) (dependent/outcome variable) given known values of \(X\) (independent/predictor/covariate/explanatory variable(s)). People will also say that we are “regressing \(Y\) on \(X\)”.
You may have seen the formula for a line written like this:
\[Y = mX + b\]
\[Y = aX + b\]
In this case \(m\) or \(a\) is the slope of the line and \(b\) is a constant and represents the y-intercept or the point where the y axis is crossed by the line, when \(x = 0\).
We can also write this model like this:
\[Y = \beta_{1}X +\beta_{0}\]
Now \(\beta_{1}\), called Beta one, is our slope notation and \(\beta_{0}\), Beta zero (also called Beta naught), is our intercept notation.
Importantly the slope (\(m\) or \(a\) or \(\beta_{1}\)) gives us a measure of the strength of the influence of the independent variable (\(X\)) on the dependent variable (\(Y\)).
Check out this interactive explanation of how the ordinary least squares method works.
Here is an image of what we are saying about the ordinary least squares regression to fit a line to data:
In some cases we can fit a line perfectly and all points will lie on the line with no distance to the line:
In this case, the slope or \(\beta_{1}\) is 1 and the intercept \(\beta_{0}\) is 10 and every observed data point lies exactly on the line, e.g., we can see that when \(X\) is 50, \(Y\) is exactly 60. This is very unusual in statistical analysis however, as often the relationship between variables is more complicated and there is more noise in our data. In these other cases there will be greater distances between the line and the points.
Like this regression: 
In this case, because there is some vertical distance between the line and the data points, there is a bit of what is called “error” in the model. The formula for the relationship between \(X\) and \(Y\) does not perfectly describe the data. The vertical distance between the line and each data point is what we call a residual. Our least squares method finds the line with the minimized value of the sum of the squared residual values.
This concept has been extended to allow for comparisons of different types of outcomes (the y’s), to involve various numbers of covariates (independent variables - the x’s), and to allow for different shapes of lines. For a guide on how to perform regressions in R see here.
In R we indicate a linear model like this:
Here our response/outcome variable is on the left of the ~ while our covariates/explanatory variables are on the right of the ~.
So let’s get back to our data…What types of outcomes do we have?
Our outcomes (\(Y\)):
We can evaluate the raw consumption or the percent of optimal consumption values that we calculated. These outcomes would all be what we call continuous variables because our values can take on any numeric value within the range of possible values. Binary outcomes, in contrast, have only two possible categorical values. We can also evaluate our binary outcome of if the optimal level of consumption was achieved or not.
Continuous outcomes can be evaluated with various types of linear regressions, while binary outcomes can be evaluated with what is called logistic regression. See here for a guide on different types of regression methods.
We have both continuous outcomes of raw consumption rates or percentages of consumption relative to the optimal guideline amounts as well as binary outcomes of “yes” or “no” about whether a population achieved the optimal amount of consumption.
Our covariates (\(X_{i}\)):
We have data about age group, sex, and region information for all of our outcomes.
Again we are interested in comparing the sexes, the different age groups, and the different countries for their consumption of the different dietary factors. We can apply regression to determine if there is any influence of these group identities on the dietary consumption outcomes.
Before we get started, let’s remove the global values from our data and set them aside, as this is really a composite of all the country values.
Let’s focus on the dietary factors that appeared to potentially have a difference between genders based on our figure in our exploratory analysis.
“If we just look at differences by sex for the specific dietary factors, males appear to potentially consume more of many of the factors, including possibly more sodium, fiber, calcium, red meat, and sugar-sweetened beverages than females. Females may consume more fruit.”
Let’s take a look at red meat.
We can compare the red meat consumption of males and females around the world using the well known \(t\)-test using the t.test() function and a linear regression model using the lm() function (both are included in stats package that is installed with R by default) and we will get the same results. See here for additional explanation about why that is the case. Here and here are also great sources about how many commonly known statistical tests are specialized forms of regression.
Before we get started, let’s think about the assumptions of both tests.
We can evaluate if our data are normally distributed by plotting the distribution and by creating Q-Q plots.
If our data are not normally distributed, we can consider these options:
See this case study for more information on \(t\)-test assumptions.
L (linear) - There is a linear relationship between the variables.
I (independent) - The samples are independent from one another.
N (normal) - The residuals are normally distributed. Note that the variables themselves do not need to be normally distributed.
E (equal variances) - The variance of the residuals for the groups is similar. This is called homoscedasticity. In other words the residuals are of similar size along the line.
It’s also important that if there are multiple predictor variables, that these are not correlated.
See here for additional information about the assumptions of linear regressions.
In order to apply a statistical test to compare the means, one of the first things to do is to explore the frequency of the different observed values. One way to summarize the frequency of different observed values is the frequency distribution, which can be shown in a table or a plot. See here for more information about distributions.
We will use the geom_histogram() of the ggplot2package to create a histogram to evaluate the frequency distributions of our data. The facet_wrap() function of the ggplot2 package allows us to look at different parts of our data in separate plots.
diet_and_guidelines %>%
filter(food == "red meat") %>%
ggplot(aes(x = Relative_Percent)) +
geom_histogram() +
facet_wrap(~ sex) 
Now we add another dimension to our facet with the plus sign. Now it becomes very useful that we reformatted our data to the long format, as it makes it easy to facet across the different percentages that we calculated. We can specify that we want to have different Y axis scales using the scales argument as: scales = "free".
diet_and_guidelines_long %>%
filter(food == "red meat") %>%
ggplot(aes(x = percent)) +
geom_histogram() +
facet_wrap(~ percent_type + sex, scales = "free")
diet_and_guidelines_long %>%
filter(food == "red meat") %>%
ggplot(aes(sample = percent)) +
facet_wrap(~ percent_type + sex) +
geom_qq() +
geom_qq_line()
Looks like our data is right skewed.
We can transform our data to make it more normally distributed. When data is highly right skewed, a log transformation is often helpful.
Let’s take a look a the log of our percent of optimal consumption values.
diet_and_guidelines_long %>%
filter(food == "red meat") %>%
ggplot(aes(x = log10(percent))) +
geom_histogram() +
facet_wrap(~ percent_type)
diet_and_guidelines_long %>%
filter(food == "red meat") %>%
ggplot(aes(sample = log10(percent))) +
facet_wrap(~ percent_type) +
geom_qq() +
geom_qq_line()
OK, so we can get our data to look fairly normal. It isn’t perfect, but we have a large number of samples, so this is good for our \(t\)-test assumptions. The other thing we need to check is if the variance in red meat consumption is similar between the two genders. We can use the var.test() of the stats package using the log normalized data, as this data is fairly normally distributed. We could also use the mood.test on the raw data which is skewed.
mood.test(pull(filter(diet_and_guidelines,
food == "red meat"), Relative_Percent),
pull(filter(diet_and_guidelines,
food == "red meat"), sex))
Mood two-sample test of scale
data: pull(filter(diet_and_guidelines, food == "red meat"), Relative_Percent) and pull(filter(diet_and_guidelines, food == "red meat"), sex)
Z = NaN, p-value = NA
alternative hypothesis: two.sidedvar.test(log10(pull(filter(diet_and_guidelines,
food == "red meat"), Relative_Percent)) ~
pull(filter(diet_and_guidelines,
food == "red meat"), sex))
F test to compare two variances
data: log10(pull(filter(diet_and_guidelines, food == "red meat"), Relative_Percent)) by pull(filter(diet_and_guidelines, food == "red meat"), sex)
F = 0.96369, num df = 194, denom df = 194, p-value = 0.797
alternative hypothesis: true ratio of variances is not equal to 1
95 percent confidence interval:
0.7266925 1.2779719
sample estimates:
ratio of variances
0.963687 The p value > .05 for both tests, thus we can conclude that there is not enough evidence to reject the null (no difference in the spread of the distributions), thus we conclude that variance is roughly equal. Great, so we are in pretty good shape for both the \(t\)-test and the linear regression.
So now we can compare the consumption of red meat by both genders using both a \(t\)-test and a linear regression. We can use the base plot() function to get information about our residuals. This helps us to assess if we are violating our linear regression assumptions. Typically we would first check the nontransformed data, because it is not required that the predictor variables have a normal distribution. However to allow for our comparison with the \(t\)-test, we will assess the log transformed Relative_Percent data. To do this we first need to specify our model using lm(). Because we are piping in our data to our model, we need to indicate that this is the data we intend to use by using . for the data argument.
diet_and_guidelines %>%
filter(food == "red meat") %>%
lm(log10(Relative_Percent) ~ sex, data = .) %>%
plot()
Our second plot shows us that our residuals are a bit negatively or left skewed, in this case just like the Relative_Percent data. However, linear regression is relatively robust to tailed distributions and again for the sake of comparison we will show the results here. It is possible that perhaps another term needs to be included in our model to improve this. We will check this in a bit.
We can see that our residuals are similar between males and females, as the first and third plot show similar values in the two lines. This is suggestive of homoscedasticity. This like our variance test gives us information about the differences in variance between the two groups. Here is what the the plots would look like if the variance were not the same:
diet_and_guidelines %>%
filter(food == "red meat") %>%
mutate(example_perc =case_when(
# this will artifically make our female data have different
# varaince from the male data
sex == "Female" ~ log(Relative_Percent),
sex == "Male" ~ Relative_Percent)) %>%
lm(log10(example_perc) ~ sex, data = .) %>%
plot()
OK, so now we will evaluate the relationship between sex identity and Relative_Percent consumption of red meat using both a \(t\)-test and a linear regression:
diet_and_guidelines %>%
filter(food == "red meat") %>%
t.test(log10(Relative_Percent) ~ sex, data = ., var.equal = TRUE)
Two Sample t-test
data: log10(Relative_Percent) by sex
t = -5.3187, df = 388, p-value = 1.77e-07
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.2525474 -0.1162266
sample estimates:
mean in group Female mean in group Male
1.798872 1.983259 diet_and_guidelines %>%
filter(food == "red meat") %>%
lm(log10(Relative_Percent) ~ sex, data = .) %>%
summary()
Call:
lm(formula = log10(Relative_Percent) ~ sex, data = .)
Residuals:
Min 1Q Median 3Q Max
-1.0238 -0.2484 0.0052 0.3127 0.6170
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.79887 0.02451 73.382 < 2e-16 ***
sexMale 0.18439 0.03467 5.319 1.77e-07 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.3423 on 388 degrees of freedom
Multiple R-squared: 0.06795, Adjusted R-squared: 0.06555
F-statistic: 28.29 on 1 and 388 DF, p-value: 1.77e-07Notice how the \(t\) value and the p-value are the same! (Well almost, the \(t\) value is negative in the t.test() output because the female group is being used as reference group, while the male group is being used as the reference group in lm()). We can fix this using the fct_inorder() function of the forcats package which is all about factors. This function allows us to order the factor by what appears first. In this case “male” appears first, so now our output will match that of the lm() function.
diet_and_guidelines %<>%
mutate_at(vars(sex), factor)
diet_and_guidelines %>%
filter(food == "red meat") %>%
lm(log10(Relative_Percent) ~ sex, data = .) %>%
summary()
Call:
lm(formula = log10(Relative_Percent) ~ sex, data = .)
Residuals:
Min 1Q Median 3Q Max
-1.0238 -0.2484 0.0052 0.3127 0.6170
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.79887 0.02451 73.382 < 2e-16 ***
sexMale 0.18439 0.03467 5.319 1.77e-07 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.3423 on 388 degrees of freedom
Multiple R-squared: 0.06795, Adjusted R-squared: 0.06555
F-statistic: 28.29 on 1 and 388 DF, p-value: 1.77e-07diet_and_guidelines %>%
mutate_at(vars(sex), forcats::fct_inorder) %>%
filter(food == "red meat") %>%
t.test(log10(Relative_Percent) ~ sex, data = .,
var.equal = TRUE)
Two Sample t-test
data: log10(Relative_Percent) by sex
t = 5.3187, df = 388, p-value = 1.77e-07
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
0.1162266 0.2525474
sample estimates:
mean in group Male mean in group Female
1.983259 1.798872 Now they match. Notice that the degrees of freedom also match, both results show 388 degrees of freedom. We are estimating two parameters for the linear model the two \(\beta\) coefficients, (the slope and intercept), and for the \(t\)-test we are estimating the means of two groups (males and females). Overall we have two samples (male and female) for each of the 195 countries.
Thus, the overall sample number is: \(n = 195*2 = 390\)
\[df = n - # parameters estimating\] Thus the degrees of freedom can be calculated as: \(df = 390 -2 = 388\)
In the lm() output, the Beta zero (\(\beta_{0}\)) which can be interpreted as the intercept or the mean value when sex is not male (so in this case when sex is female) has the same value as the calculated mean of in the t.test() output. The Beta one (\(\beta_{1}\)) can be interpreted as the slope of the regression line or the difference between the means of the two groups. We can also think of this as the change in \(Y\) with one unit change in \(X\).
If we subtract the means calculated in the t.test() output, we get the value of \(\beta_{1}\) (the slope or the sexMale estimate) of the lm() output!
Mean of males - Mean of females \(1.983259 - 1.798872 =0.184387\)
Cool!
For more information about the output of lm() see here.
Let’s plot the data to see what is happening:
diet_and_guidelines %>%
filter(food == "red meat") %>%
ggplot(aes(x = sex, y = log10(Relative_Percent))) +
geom_boxplot(outlier.shape = NA) +
geom_jitter(width = .2) +
stat_summary(fun.y = mean,
colour = "blue",
geom = "point",
shape = 18,
size = 3) +
# using the slope and intercept from the lm output
geom_abline(intercept = 1.79887,
slope = 0.18439,
color = "red") +
# to draw a line between the boxplots
stat_summary(fun.y = mean,
geom = "line",
group = 1,
color = "blue",
size = 1.5)
We can see that the slope of the linear model is equivalent to the difference between the mean of the two groups, as the linear model line is parallel to the line drawn between the two means.
Also, since we have female and male values each from the same countries, our data is what we call “paired”. The values from the same country could be related to one another because of cultural difference in diet, thus the assumption of the unpaired \(t\)-test that the samples are independent is violated. Therefore, we need to model our data in a way that accounts for this. First we will use the pivot_wider() function of the tidyr package to get our data in a format that is easier to use for this purpose. To use this function we specify the values that we want to separate into more variables using the values_from argument and we use the names_from argument to specify how we want to separate these other variables. In this case we will make a male and female version of all the other variables specified.
wide_diet <- diet_and_guidelines %>%
pivot_wider(values_from = c(contains("percent"),
mean,
upper,
lower,
opt_achieved),
names_from = sex)
glimpse(wide_diet)Rows: 2,925
Columns: 27
$ year_id <dbl> 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2…
$ location_name <chr> "China", "China", "China", "China", "China"…
$ rei_id <dbl> 111, 112, 113, 114, 115, 116, 117, 118, 119…
$ food <chr> "fruits", "vegetables", "whole grains", "nu…
$ age_group_name <chr> "All Available Ages", "All Available Ages",…
$ parameter <chr> "continuous", "continuous", "continuous", "…
$ unit <chr> "g/day", "g/day", "g/day", "g/day", "g/day"…
$ direction <chr> "low", "low", "low", "low", "low", "high", …
$ lower_optimal <dbl> 200.0, 290.0, 100.0, 16.0, 350.0, 18.0, 0.0…
$ optimal <dbl> 250.00, 360.00, 125.00, 21.00, 435.00, 23.0…
$ upper_optimal <dbl> 300.0, 430.0, 150.0, 25.0, 520.0, 27.0, 4.0…
$ unit_optimal <chr> "g", "g", "g", "g", "g", "g", "g", "g", "g"…
$ food_to_plot <chr> "fruits", "vegetables", "whole\ngrains", "n…
$ Relative_Percent_Male <dbl> 30.1276714, 69.9780163, 20.5969990, 9.55243…
$ Relative_Percent_Female <dbl> 34.3698972, 65.4711643, 19.2030299, 8.98597…
$ range_percent_Male <dbl> 37.6595893, 86.8692617, 25.7462488, 12.5375…
$ range_percent_Female <dbl> 42.9623715, 81.2745488, 24.0037874, 11.7940…
$ percent_over_under_Male <dbl> -62.34041, -13.13074, -74.25375, -87.46243,…
$ percent_over_under_Female <dbl> -57.03763, -18.72545, -75.99621, -88.20590,…
$ mean_Male <dbl> 7.531918e+01, 2.519209e+02, 2.574625e+01, 2…
$ mean_Female <dbl> 8.592474e+01, 2.356962e+02, 2.400379e+01, 1…
$ upper_Male <dbl> 8.658295e+01, 2.946774e+02, 2.723223e+01, 2…
$ upper_Female <dbl> 9.759191e+01, 2.720864e+02, 2.541951e+01, 2…
$ lower_Male <dbl> 6.543868e+01, 2.153508e+02, 2.426562e+01, 1…
$ lower_Female <dbl> 7.531231e+01, 2.033138e+02, 2.262773e+01, 1…
$ opt_achieved_Male <chr> "No", "No", "No", "No", "No", "No", "Yes", …
$ opt_achieved_Female <chr> "No", "No", "No", "No", "No", "No", "Yes", …Now will perform the paired versions of our tests. This is very easy to do with the t.test() function, by simply using the paired argument and setting it equal to TRUE.
The paired version of the linear model is a bit more complex. In this case we will add another term in our model to evaluate the influence of sex on Relative_Percent consumption while keeping the country identity fixed or constant, or in other words controlling/adjusting for country. We can use the + to add this additional term. Now that we have multiple covariate/explanatory variable terms, we would call this a multiple linear regression.
So now our model in words will be:
Mean relative consumption of red meat is dependent on sex and country. Or in other words, sex and location infleunce the consumption of red meat of different populations around the world.
Then the coefficient for sex will be different from what we had in our previous lm() model, as it will be calculated while keeping location_name or the country where the consumption value was obtained fixed, or in other words “controlling for location_name.” This will also result in output for each of the countries. The coefficients here represent the average consumption value for each country while accounting for sex.
First let’s take a look at our plots for this new model:
diet_and_guidelines %<>%
mutate_at(vars(location_name), factor)
plot(lm(
log10(pull(filter(diet_and_guidelines, food == "red meat"),
Relative_Percent)) ~
pull(filter(diet_and_guidelines, food == "red meat"),
sex) +
pull(filter(diet_and_guidelines, food == "red meat"),
location_name)))
This looks much better than our previous plots.
This guide provides more information on how to interpret these residual diagnostic plots.
Based on our Q-Q plot, we appear to have some outliers perhaps at the extreme ends of our tails but overall the residuals look fairly normal. The residual vs fitted plot shows us if the relationship between our outcome variable and our predictors looks linear, if there is unequal error variance between groups, and if there are possible outliers. Ideally this should look like a band of points equally centered around zero. Here are examples of these plots that might show issues of concern.
Overall our plot looks fairly good. The shape of our data looks fairly linear (the residuals don’t appear to have a shape other than a band or line), there does not appear to be any extreme outliers (no data points are especially far away) and the points have the same general range around the line for the various fitted values. There are a few points with wider residuals at the higher fitted values, but overall this looks quite reasonable.
Our scale-location plot also shows us that our variance looks fairly equal across groups as our values show a relatively even spread. A larger bend in the line would indicate more variation in the variance across our independent variable groups also known as heteroscedasticity. There is only a slight bend in the line for our data suggestive of homoscedasticity. So our assumptions look pretty good:
So now we will get the sumary for our model:
summary(lm(
log10(pull(filter(diet_and_guidelines, food == "red meat"),
Relative_Percent)) ~
pull(filter(diet_and_guidelines, food == "red meat"),
sex) +
pull(filter(diet_and_guidelines, food == "red meat"),
location_name)))
Call:
lm(formula = log10(pull(filter(diet_and_guidelines, food == "red meat"),
Relative_Percent)) ~ pull(filter(diet_and_guidelines, food ==
"red meat"), sex) + pull(filter(diet_and_guidelines, food ==
"red meat"), location_name))
Residuals:
Min 1Q Median 3Q Max
-0.031341 -0.003897 0.000000 0.003897 0.031341
Coefficients:
Estimate
(Intercept) 1.537017
pull(filter(diet_and_guidelines, food == "red meat"), sex)Male 0.184387
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Albania 0.438021
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Algeria 0.045488
pull(filter(diet_and_guidelines, food == "red meat"), location_name)American Samoa 0.467181
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Andorra 0.748631
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Angola 0.250455
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Antigua and Barbuda 0.299310
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Argentina 0.824071
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Armenia 0.394447
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Australia 0.814642
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Austria 0.870921
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Azerbaijan 0.293968
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Bahrain 0.289155
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Bangladesh -0.611390
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Barbados 0.310247
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Belarus 0.687041
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Belgium 0.676956
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Belize 0.176578
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Benin -0.326705
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Bermuda 0.424542
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Bhutan -0.120195
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Bolivia 0.493009
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Bosnia and Herzegovina 0.170649
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Botswana 0.133756
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Brazil 0.740470
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Brunei 0.221789
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Bulgaria 0.680417
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Burkina Faso 0.092972
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Burundi -0.196961
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Cambodia 0.110614
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Cameroon -0.055387
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Canada 0.667331
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Cape Verde 0.394975
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Central African Republic 0.369231
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Chad 0.043477
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Chile 0.670126
pull(filter(diet_and_guidelines, food == "red meat"), location_name)China 0.634103
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Colombia 0.360716
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Comoros -0.115892
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Congo -0.257950
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Costa Rica 0.379751
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Cote d'Ivoire -0.387071
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Croatia 0.581435
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Cuba 0.347647
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Cyprus 0.600760
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Czech Republic 0.647519
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Democratic Republic of the Congo -0.140305
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Denmark 0.662793
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Djibouti 0.204672
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Dominica 0.196880
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Dominican Republic 0.279081
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Ecuador 0.516439
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Egypt 0.138031
pull(filter(diet_and_guidelines, food == "red meat"), location_name)El Salvador 0.043217
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Equatorial Guinea 0.415090
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Eritrea -0.141927
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Estonia 0.541948
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Ethiopia -0.173354
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Federated States of Micronesia 0.209379
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Fiji 0.297535
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Finland 0.625494
pull(filter(diet_and_guidelines, food == "red meat"), location_name)France 0.700810
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Gabon 0.187008
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Georgia 0.228131
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Germany 0.703028
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Ghana -0.346773
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Greece 0.667048
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Greenland 0.768537
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Grenada 0.213445
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Guam 0.577515
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Guatemala 0.009005
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Guinea -0.194870
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Guinea-Bissau 0.133387
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Guyana -0.216393
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Haiti -0.051033
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Honduras 0.117781
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Hungary 0.610661
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Iceland 0.659366
pull(filter(diet_and_guidelines, food == "red meat"), location_name)India -0.650568
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Indonesia -0.219020
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Iran -0.045613
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Iraq -0.318520
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Ireland 0.800964
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Israel 0.483992
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Italy 0.663507
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Jamaica -0.033539
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Japan 0.374311
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Jordan 0.109734
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Kazakhstan 0.657682
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Kenya 0.104881
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Kiribati 0.130670
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Kuwait 0.365667
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Kyrgyzstan 0.424414
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Laos 0.224672
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Latvia 0.573087
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Lebanon 0.330736
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Lesotho 0.078852
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Liberia -0.380804
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Libya 0.101999
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Lithuania 0.653012
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Luxembourg 0.801064
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Macedonia 0.315642
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Madagascar 0.010171
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Malawi -0.240867
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Malaysia 0.126939
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Maldives -0.131439
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Mali 0.252462
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Malta 0.654370
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Marshall Islands 0.242041
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Mauritania 0.253405
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Mauritius 0.084586
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Mexico 0.493796
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Moldova 0.248523
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Mongolia 0.802313
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Montenegro 0.695318
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Morocco 0.046481
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Mozambique -0.146113
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Myanmar 0.161358
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Namibia 0.271516
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Nepal -0.003694
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Netherlands 0.689104
pull(filter(diet_and_guidelines, food == "red meat"), location_name)New Zealand 0.792965
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Nicaragua -0.108988
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Niger 0.191332
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Nigeria -0.169992
pull(filter(diet_and_guidelines, food == "red meat"), location_name)North Korea -0.246185
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Northern Mariana Islands 0.435022
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Norway 0.663406
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Oman 0.377107
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Pakistan 0.014624
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Palestine -0.002820
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Panama 0.477302
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Papua New Guinea 0.177936
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Paraguay 0.601524
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Peru -0.037011
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Philippines 0.344310
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Poland 0.687177
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Portugal 0.733722
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Puerto Rico 0.412028
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Qatar 0.429955
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Romania 0.652514
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Russian Federation 0.535982
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Rwanda -0.277812
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Saint Lucia 0.212165
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Saint Vincent and the Grenadines 0.196704
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Samoa 0.267956
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Sao Tome and Principe -0.047627
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Saudi Arabia 0.057527
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Senegal -0.072896
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Serbia 0.508400
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Seychelles 0.301701
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Sierra Leone -0.522458
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Singapore 0.582594
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Slovakia 0.627148
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Slovenia 0.679024
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Solomon Islands -0.010315
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Somalia -0.256388
pull(filter(diet_and_guidelines, food == "red meat"), location_name)South Africa 0.435449
pull(filter(diet_and_guidelines, food == "red meat"), location_name)South Korea 0.592893
pull(filter(diet_and_guidelines, food == "red meat"), location_name)South Sudan 0.015340
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Spain 0.678441
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Sri Lanka -0.749966
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Sudan -0.050768
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Suriname 0.176747
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Swaziland 0.301692
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Sweden 0.692026
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Switzerland 0.701932
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Syria -0.011904
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Taiwan 0.677543
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Tajikistan 0.015453
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Tanzania -0.129507
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Thailand 0.164882
pull(filter(diet_and_guidelines, food == "red meat"), location_name)The Bahamas 0.577812
pull(filter(diet_and_guidelines, food == "red meat"), location_name)The Gambia -0.389047
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Timor-Leste 0.060600
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Togo -0.308629
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Tonga 0.289031
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Trinidad and Tobago 0.160447
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Tunisia -0.005090
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Turkey 0.050119
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Turkmenistan 0.722890
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Uganda 0.015166
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Ukraine 0.372418
pull(filter(diet_and_guidelines, food == "red meat"), location_name)United Arab Emirates 0.283428
pull(filter(diet_and_guidelines, food == "red meat"), location_name)United Kingdom 0.556010
pull(filter(diet_and_guidelines, food == "red meat"), location_name)United States 0.708984
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Uruguay 0.632295
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Uzbekistan 0.458726
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Vanuatu 0.191061
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Venezuela 0.439508
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Vietnam 0.494979
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Virgin Islands, U.S. 0.438211
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Yemen -0.126839
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Zambia -0.104306
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Zimbabwe 0.046567
Std. Error
(Intercept) 0.006860
pull(filter(diet_and_guidelines, food == "red meat"), sex)Male 0.000980
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Albania 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Algeria 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)American Samoa 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Andorra 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Angola 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Antigua and Barbuda 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Argentina 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Armenia 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Australia 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Austria 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Azerbaijan 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Bahrain 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Bangladesh 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Barbados 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Belarus 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Belgium 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Belize 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Benin 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Bermuda 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Bhutan 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Bolivia 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Bosnia and Herzegovina 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Botswana 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Brazil 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Brunei 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Bulgaria 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Burkina Faso 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Burundi 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Cambodia 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Cameroon 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Canada 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Cape Verde 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Central African Republic 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Chad 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Chile 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)China 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Colombia 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Comoros 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Congo 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Costa Rica 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Cote d'Ivoire 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Croatia 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Cuba 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Cyprus 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Czech Republic 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Democratic Republic of the Congo 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Denmark 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Djibouti 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Dominica 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Dominican Republic 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Ecuador 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Egypt 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)El Salvador 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Equatorial Guinea 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Eritrea 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Estonia 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Ethiopia 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Federated States of Micronesia 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Fiji 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Finland 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)France 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Gabon 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Georgia 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Germany 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Ghana 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Greece 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Greenland 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Grenada 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Guam 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Guatemala 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Guinea 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Guinea-Bissau 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Guyana 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Haiti 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Honduras 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Hungary 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Iceland 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)India 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Indonesia 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Iran 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Iraq 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Ireland 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Israel 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Italy 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Jamaica 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Japan 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Jordan 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Kazakhstan 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Kenya 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Kiribati 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Kuwait 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Kyrgyzstan 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Laos 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Latvia 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Lebanon 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Lesotho 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Liberia 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Libya 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Lithuania 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Luxembourg 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Macedonia 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Madagascar 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Malawi 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Malaysia 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Maldives 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Mali 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Malta 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Marshall Islands 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Mauritania 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Mauritius 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Mexico 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Moldova 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Mongolia 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Montenegro 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Morocco 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Mozambique 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Myanmar 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Namibia 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Nepal 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Netherlands 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)New Zealand 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Nicaragua 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Niger 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Nigeria 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)North Korea 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Northern Mariana Islands 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Norway 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Oman 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Pakistan 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Palestine 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Panama 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Papua New Guinea 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Paraguay 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Peru 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Philippines 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Poland 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Portugal 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Puerto Rico 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Qatar 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Romania 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Russian Federation 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Rwanda 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Saint Lucia 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Saint Vincent and the Grenadines 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Samoa 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Sao Tome and Principe 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Saudi Arabia 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Senegal 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Serbia 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Seychelles 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Sierra Leone 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Singapore 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Slovakia 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Slovenia 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Solomon Islands 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Somalia 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)South Africa 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)South Korea 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)South Sudan 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Spain 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Sri Lanka 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Sudan 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Suriname 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Swaziland 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Sweden 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Switzerland 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Syria 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Taiwan 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Tajikistan 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Tanzania 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Thailand 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)The Bahamas 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)The Gambia 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Timor-Leste 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Togo 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Tonga 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Trinidad and Tobago 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Tunisia 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Turkey 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Turkmenistan 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Uganda 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Ukraine 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)United Arab Emirates 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)United Kingdom 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)United States 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Uruguay 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Uzbekistan 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Vanuatu 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Venezuela 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Vietnam 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Virgin Islands, U.S. 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Yemen 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Zambia 0.009676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Zimbabwe 0.009676
t value
(Intercept) 224.066
pull(filter(diet_and_guidelines, food == "red meat"), sex)Male 188.159
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Albania 45.268
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Algeria 4.701
pull(filter(diet_and_guidelines, food == "red meat"), location_name)American Samoa 48.281
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Andorra 77.368
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Angola 25.884
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Antigua and Barbuda 30.932
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Argentina 85.164
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Armenia 40.764
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Australia 84.190
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Austria 90.006
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Azerbaijan 30.380
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Bahrain 29.883
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Bangladesh -63.185
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Barbados 32.063
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Belarus 71.003
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Belgium 69.961
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Belize 18.249
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Benin -33.764
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Bermuda 43.875
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Bhutan -12.422
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Bolivia 50.950
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Bosnia and Herzegovina 17.636
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Botswana 13.823
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Brazil 76.525
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Brunei 22.921
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Bulgaria 70.318
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Burkina Faso 9.608
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Burundi -20.355
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Cambodia 11.432
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Cameroon -5.724
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Canada 68.966
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Cape Verde 40.819
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Central African Republic 38.159
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Chad 4.493
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Chile 69.255
pull(filter(diet_and_guidelines, food == "red meat"), location_name)China 65.532
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Colombia 37.279
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Comoros -11.977
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Congo -26.658
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Costa Rica 39.246
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Cote d'Ivoire -40.002
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Croatia 60.089
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Cuba 35.928
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Cyprus 62.086
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Czech Republic 66.918
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Democratic Republic of the Congo -14.500
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Denmark 68.497
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Djibouti 21.152
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Dominica 20.347
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Dominican Republic 28.842
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Ecuador 53.372
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Egypt 14.265
pull(filter(diet_and_guidelines, food == "red meat"), location_name)El Salvador 4.466
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Equatorial Guinea 42.898
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Eritrea -14.668
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Estonia 56.008
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Ethiopia -17.915
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Federated States of Micronesia 21.638
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Fiji 30.749
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Finland 64.642
pull(filter(diet_and_guidelines, food == "red meat"), location_name)France 72.426
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Gabon 19.326
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Georgia 23.576
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Germany 72.655
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Ghana -35.838
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Greece 68.937
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Greenland 79.425
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Grenada 22.059
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Guam 59.684
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Guatemala 0.931
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Guinea -20.139
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Guinea-Bissau 13.785
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Guyana -22.363
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Haiti -5.274
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Honduras 12.172
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Hungary 63.109
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Iceland 68.143
pull(filter(diet_and_guidelines, food == "red meat"), location_name)India -67.234
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Indonesia -22.635
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Iran -4.714
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Iraq -32.918
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Ireland 82.776
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Israel 50.019
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Italy 68.571
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Jamaica -3.466
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Japan 38.683
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Jordan 11.341
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Kazakhstan 67.969
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Kenya 10.839
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Kiribati 13.504
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Kuwait 37.790
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Kyrgyzstan 43.861
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Laos 23.219
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Latvia 59.226
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Lebanon 34.180
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Lesotho 8.149
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Liberia -39.355
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Libya 10.541
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Lithuania 67.486
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Luxembourg 82.787
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Macedonia 32.620
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Madagascar 1.051
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Malawi -24.893
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Malaysia 13.119
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Maldives -13.584
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Mali 26.091
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Malta 67.626
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Marshall Islands 25.014
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Mauritania 26.188
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Mauritius 8.742
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Mexico 51.032
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Moldova 25.684
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Mongolia 82.916
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Montenegro 71.858
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Morocco 4.804
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Mozambique -15.100
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Myanmar 16.676
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Namibia 28.060
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Nepal -0.382
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Netherlands 71.216
pull(filter(diet_and_guidelines, food == "red meat"), location_name)New Zealand 81.950
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Nicaragua -11.263
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Niger 19.773
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Nigeria -17.568
pull(filter(diet_and_guidelines, food == "red meat"), location_name)North Korea -25.442
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Northern Mariana Islands 44.958
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Norway 68.560
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Oman 38.972
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Pakistan 1.511
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Palestine -0.291
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Panama 49.327
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Papua New Guinea 18.389
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Paraguay 62.165
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Peru -3.825
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Philippines 35.583
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Poland 71.017
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Portugal 75.827
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Puerto Rico 42.581
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Qatar 44.434
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Romania 67.435
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Russian Federation 55.392
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Rwanda -28.711
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Saint Lucia 21.926
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Saint Vincent and the Grenadines 20.329
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Samoa 27.692
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Sao Tome and Principe -4.922
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Saudi Arabia 5.945
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Senegal -7.534
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Serbia 52.541
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Seychelles 31.180
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Sierra Leone -53.994
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Singapore 60.209
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Slovakia 64.813
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Slovenia 70.174
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Solomon Islands -1.066
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Somalia -26.497
pull(filter(diet_and_guidelines, food == "red meat"), location_name)South Africa 45.002
pull(filter(diet_and_guidelines, food == "red meat"), location_name)South Korea 61.273
pull(filter(diet_and_guidelines, food == "red meat"), location_name)South Sudan 1.585
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Spain 70.114
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Sri Lanka -77.506
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Sudan -5.247
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Suriname 18.266
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Swaziland 31.179
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Sweden 71.518
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Switzerland 72.542
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Syria -1.230
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Taiwan 70.021
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Tajikistan 1.597
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Tanzania -13.384
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Thailand 17.040
pull(filter(diet_and_guidelines, food == "red meat"), location_name)The Bahamas 59.715
pull(filter(diet_and_guidelines, food == "red meat"), location_name)The Gambia -40.206
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Timor-Leste 6.263
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Togo -31.896
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Tonga 29.870
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Trinidad and Tobago 16.582
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Tunisia -0.526
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Turkey 5.180
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Turkmenistan 74.708
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Uganda 1.567
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Ukraine 38.488
pull(filter(diet_and_guidelines, food == "red meat"), location_name)United Arab Emirates 29.291
pull(filter(diet_and_guidelines, food == "red meat"), location_name)United Kingdom 57.461
pull(filter(diet_and_guidelines, food == "red meat"), location_name)United States 73.271
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Uruguay 65.345
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Uzbekistan 47.407
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Vanuatu 19.745
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Venezuela 45.421
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Vietnam 51.154
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Virgin Islands, U.S. 45.287
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Yemen -13.108
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Zambia -10.780
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Zimbabwe 4.812
Pr(>|t|)
(Intercept) < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), sex)Male < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Albania < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Algeria 4.90e-06
pull(filter(diet_and_guidelines, food == "red meat"), location_name)American Samoa < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Andorra < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Angola < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Antigua and Barbuda < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Argentina < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Armenia < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Australia < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Austria < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Azerbaijan < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Bahrain < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Bangladesh < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Barbados < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Belarus < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Belgium < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Belize < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Benin < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Bermuda < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Bhutan < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Bolivia < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Bosnia and Herzegovina < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Botswana < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Brazil < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Brunei < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Bulgaria < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Burkina Faso < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Burundi < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Cambodia < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Cameroon 3.90e-08
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Canada < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Cape Verde < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Central African Republic < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Chad 1.20e-05
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Chile < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)China < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Colombia < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Comoros < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Congo < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Costa Rica < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Cote d'Ivoire < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Croatia < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Cuba < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Cyprus < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Czech Republic < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Democratic Republic of the Congo < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Denmark < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Djibouti < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Dominica < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Dominican Republic < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Ecuador < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Egypt < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)El Salvador 1.35e-05
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Equatorial Guinea < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Eritrea < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Estonia < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Ethiopia < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Federated States of Micronesia < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Fiji < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Finland < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)France < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Gabon < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Georgia < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Germany < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Ghana < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Greece < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Greenland < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Grenada < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Guam < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Guatemala 0.353203
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Guinea < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Guinea-Bissau < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Guyana < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Haiti 3.54e-07
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Honduras < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Hungary < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Iceland < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)India < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Indonesia < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Iran 4.63e-06
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Iraq < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Ireland < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Israel < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Italy < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Jamaica 0.000650
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Japan < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Jordan < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Kazakhstan < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Kenya < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Kiribati < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Kuwait < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Kyrgyzstan < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Laos < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Latvia < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Lebanon < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Lesotho 4.43e-14
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Liberia < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Libya < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Lithuania < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Luxembourg < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Macedonia < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Madagascar 0.294507
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Malawi < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Malaysia < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Maldives < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Mali < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Malta < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Marshall Islands < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Mauritania < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Mauritius 1.09e-15
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Mexico < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Moldova < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Mongolia < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Montenegro < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Morocco 3.11e-06
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Mozambique < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Myanmar < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Namibia < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Nepal 0.703082
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Netherlands < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)New Zealand < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Nicaragua < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Niger < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Nigeria < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)North Korea < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Northern Mariana Islands < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Norway < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Oman < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Pakistan 0.132342
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Palestine 0.771045
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Panama < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Papua New Guinea < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Paraguay < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Peru 0.000176
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Philippines < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Poland < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Portugal < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Puerto Rico < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Qatar < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Romania < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Russian Federation < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Rwanda < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Saint Lucia < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Saint Vincent and the Grenadines < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Samoa < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Sao Tome and Principe 1.83e-06
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Saudi Arabia 1.26e-08
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Senegal 1.83e-12
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Serbia < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Seychelles < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Sierra Leone < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Singapore < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Slovakia < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Slovenia < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Solomon Islands 0.287747
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Somalia < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)South Africa < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)South Korea < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)South Sudan 0.114522
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Spain < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Sri Lanka < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Sudan 4.04e-07
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Suriname < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Swaziland < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Sweden < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Switzerland < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Syria 0.220097
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Taiwan < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Tajikistan 0.111900
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Tanzania < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Thailand < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)The Bahamas < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)The Gambia < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Timor-Leste 2.38e-09
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Togo < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Tonga < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Trinidad and Tobago < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Tunisia 0.599486
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Turkey 5.55e-07
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Turkmenistan < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Uganda 0.118652
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Ukraine < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)United Arab Emirates < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)United Kingdom < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)United States < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Uruguay < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Uzbekistan < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Vanuatu < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Venezuela < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Vietnam < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Virgin Islands, U.S. < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Yemen < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Zambia < 2e-16
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Zimbabwe 2.99e-06
(Intercept) ***
pull(filter(diet_and_guidelines, food == "red meat"), sex)Male ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Albania ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Algeria ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)American Samoa ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Andorra ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Angola ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Antigua and Barbuda ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Argentina ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Armenia ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Australia ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Austria ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Azerbaijan ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Bahrain ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Bangladesh ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Barbados ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Belarus ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Belgium ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Belize ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Benin ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Bermuda ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Bhutan ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Bolivia ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Bosnia and Herzegovina ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Botswana ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Brazil ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Brunei ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Bulgaria ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Burkina Faso ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Burundi ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Cambodia ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Cameroon ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Canada ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Cape Verde ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Central African Republic ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Chad ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Chile ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)China ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Colombia ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Comoros ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Congo ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Costa Rica ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Cote d'Ivoire ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Croatia ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Cuba ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Cyprus ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Czech Republic ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Democratic Republic of the Congo ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Denmark ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Djibouti ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Dominica ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Dominican Republic ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Ecuador ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Egypt ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)El Salvador ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Equatorial Guinea ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Eritrea ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Estonia ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Ethiopia ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Federated States of Micronesia ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Fiji ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Finland ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)France ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Gabon ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Georgia ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Germany ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Ghana ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Greece ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Greenland ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Grenada ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Guam ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Guatemala
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Guinea ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Guinea-Bissau ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Guyana ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Haiti ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Honduras ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Hungary ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Iceland ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)India ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Indonesia ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Iran ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Iraq ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Ireland ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Israel ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Italy ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Jamaica ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Japan ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Jordan ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Kazakhstan ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Kenya ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Kiribati ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Kuwait ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Kyrgyzstan ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Laos ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Latvia ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Lebanon ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Lesotho ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Liberia ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Libya ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Lithuania ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Luxembourg ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Macedonia ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Madagascar
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Malawi ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Malaysia ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Maldives ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Mali ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Malta ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Marshall Islands ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Mauritania ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Mauritius ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Mexico ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Moldova ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Mongolia ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Montenegro ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Morocco ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Mozambique ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Myanmar ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Namibia ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Nepal
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Netherlands ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)New Zealand ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Nicaragua ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Niger ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Nigeria ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)North Korea ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Northern Mariana Islands ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Norway ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Oman ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Pakistan
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Palestine
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Panama ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Papua New Guinea ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Paraguay ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Peru ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Philippines ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Poland ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Portugal ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Puerto Rico ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Qatar ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Romania ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Russian Federation ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Rwanda ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Saint Lucia ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Saint Vincent and the Grenadines ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Samoa ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Sao Tome and Principe ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Saudi Arabia ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Senegal ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Serbia ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Seychelles ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Sierra Leone ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Singapore ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Slovakia ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Slovenia ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Solomon Islands
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Somalia ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)South Africa ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)South Korea ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)South Sudan
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Spain ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Sri Lanka ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Sudan ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Suriname ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Swaziland ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Sweden ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Switzerland ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Syria
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Taiwan ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Tajikistan
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Tanzania ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Thailand ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)The Bahamas ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)The Gambia ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Timor-Leste ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Togo ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Tonga ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Trinidad and Tobago ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Tunisia
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Turkey ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Turkmenistan ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Uganda
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Ukraine ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)United Arab Emirates ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)United Kingdom ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)United States ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Uruguay ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Uzbekistan ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Vanuatu ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Venezuela ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Vietnam ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Virgin Islands, U.S. ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Yemen ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Zambia ***
pull(filter(diet_and_guidelines, food == "red meat"), location_name)Zimbabwe ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.009676 on 194 degrees of freedom
Multiple R-squared: 0.9996, Adjusted R-squared: 0.9993
F-statistic: 2671 on 195 and 194 DF, p-value: < 2.2e-16Alternatively, we can perform a slightly different regression.
In this case we will use the lmer() function of the lmerTest package. This function allows us to perform what is called a linear mixed effects regression. This type of regression is called mixed because it contains both fixed and random effects.
There are many different definitions for fixed and random effects and the difference is conceptually complex and context specific.
However in simplistic terms, fixed effects are generally speaking the variables of interest that we have reason to believe explain or predict the outcome or response variable, while random effects are those that may introduce additional variance in the influence of those predictor variables on the outcome variable. For example, they may provide information about group or batch structures within the data.
In our case, we are interested in the influence of sex on the consumption of red meat, however the identity of the country where the male and female consumption values were obtained may influence this relationship and we would like to control for that. We don’t want to model for location_name itself, but just model it’s influence on the relationship of sex on consumption of red meat. In other words, we are interested in getting a sense of how sex influences consumption rates in general and we want to account for the paired structure within our data, the fact that we have corresponding consumption values for the two sexes from different countries. The notation for including a random effect like this is 1 | variable_name. The one indicates a varying-intercept group effect, in other words we expect that the intercept may vary for each value of the variable indicated to the right of the |. So in our case, the intercept (Relative_Percent consumption when sex is assigned to the zero value - not male) may be different for each country.
Again lets see what our plots look like first:
lmer_sex_loc <- lmerTest::lmer(
log10(pull(filter(diet_and_guidelines, food == "red meat"),
Relative_Percent)) ~
pull(filter(diet_and_guidelines, food == "red meat"),
sex) +
(1| pull(filter(diet_and_guidelines, food == "red meat"),
location_name)))
plot(lmer_sex_loc)
Notice that the plots look the same as with the fixed effect model.
Let’s take a look at the results:
summary(lmerTest::lmer(
log10(pull(filter(diet_and_guidelines, food == "red meat"),
Relative_Percent)) ~
pull(filter(diet_and_guidelines, food == "red meat"),
sex) +
(1| pull(filter(diet_and_guidelines, food == "red meat"),
location_name))))Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula:
log10(pull(filter(diet_and_guidelines, food == "red meat"), Relative_Percent)) ~
pull(filter(diet_and_guidelines, food == "red meat"), sex) +
(1 | pull(filter(diet_and_guidelines, food == "red meat"),
location_name))
REML criterion at convergence: -969.5
Scaled residuals:
Min 1Q Median 3Q Max
-3.2253 -0.3914 -0.0038 0.3993 3.2527
Random effects:
Groups
pull(filter(diet_and_guidelines, food == "red meat"), location_name)
Residual
Name Variance Std.Dev.
(Intercept) 1.171e-01 0.342181
9.363e-05 0.009676
Number of obs: 390, groups:
pull(filter(diet_and_guidelines, food == "red meat"), location_name), 195
Fixed effects:
Estimate
(Intercept) 1.79887
pull(filter(diet_and_guidelines, food == "red meat"), sex)Male 0.18439
Std. Error
(Intercept) 0.02451
pull(filter(diet_and_guidelines, food == "red meat"), sex)Male 0.00098
df
(Intercept) 194.15514
pull(filter(diet_and_guidelines, food == "red meat"), sex)Male 194.00000
t value Pr(>|t|)
(Intercept) 73.38 <2e-16
pull(filter(diet_and_guidelines, food == "red meat"), sex)Male 188.16 <2e-16
(Intercept) ***
pull(filter(diet_and_guidelines, food == "red meat"), sex)Male ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr)
p((__,f="ms -0.020t.test(log10(pull(filter(wide_diet, food == "red meat"),
Relative_Percent_Male)),
log10(pull(filter(wide_diet, food == "red meat"),
Relative_Percent_Female)),
var.equal = TRUE, paired = TRUE)
Paired t-test
data: log10(pull(filter(wide_diet, food == "red meat"), Relative_Percent_Male)) and log10(pull(filter(wide_diet, food == "red meat"), Relative_Percent_Female))
t = 188.16, df = 194, p-value < 2.2e-16
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
0.1824542 0.1863197
sample estimates:
mean of the differences
0.184387 You can see that now our degrees of freedom are 194, which makes sense because now we have matched or paired values for the 195 different regions and so \(df = n-1 parameter = 195 -1 = 194\). In this case we aren’t calculating two different means in our \(t\)-test, but instead this is a one sample \(t\)-test of the differences between males and females across all the country pairs, so thus only one parameter. It is more complicated to calculate the degrees of freedom in the mixed effect model and beyond this case study, but it is based on the Satterthwaite formula and results in the same degrees of freedom.
Most importantly, again we get the same \(t\)-value and p-value with the two tests!
You can see that yet again our degrees of freedom are 194, and again we get the same \(t\)-value and p-value with all tests. In this case we also have information about how each of the countries might influence mean consumption. So the decision of which test to perform depends on your question of interest. In this case we were particularly interested in the influence of sex, so setting location_name to a random effect provides the same level of detail about sex without as much information about location_name, so that might be ideal. As we can see, the results, in this case, are the same.
We can conclude from these tests that we have enough evidence to reject the null hypothesis that there is no difference between the means or that sex has no association or influence on red meat consumption. Therefore, it appears that men consume significantly more red meat than females globally.
Let’s choose another dietary factor to compare between males and females.
diet_and_guidelines_long %>%
filter(food == "fruits") %>%
ggplot(aes(x = log10(percent))) +
geom_histogram() +
facet_wrap(~ percent_type)
diet_and_guidelines_long %>%
filter(food == "fruits") %>%
ggplot(aes(sample = log10(percent))) +
facet_wrap(~ percent_type) +
geom_qq() +
geom_qq_line()
mood.test(pull(filter(diet_and_guidelines,
food == "fruits"),
Relative_Percent),
pull(filter(diet_and_guidelines,
food == "fruits"),
sex))
Mood two-sample test of scale
data: pull(filter(diet_and_guidelines, food == "fruits"), Relative_Percent) and pull(filter(diet_and_guidelines, food == "fruits"), sex)
Z = 0, p-value = 1
alternative hypothesis: two.sidedvar.test(log10(pull(filter(diet_and_guidelines,
food == "fruits"),
Relative_Percent))~
pull(filter(diet_and_guidelines,
food == "fruits"),
sex))
F test to compare two variances
data: log10(pull(filter(diet_and_guidelines, food == "fruits"), Relative_Percent)) by pull(filter(diet_and_guidelines, food == "fruits"), sex)
F = 1.0025, num df = 194, denom df = 194, p-value = 0.9859
alternative hypothesis: true ratio of variances is not equal to 1
95 percent confidence interval:
0.7559929 1.3295002
sample estimates:
ratio of variances
1.002543 summary(lmer(
log10(pull(filter(diet_and_guidelines,
food == "fruits"),
Relative_Percent)) ~
pull(filter(diet_and_guidelines,
food == "fruits"),
sex) +
(1 | pull(filter(diet_and_guidelines,
food == "fruits"),
location_name))))Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula:
log10(pull(filter(diet_and_guidelines, food == "fruits"), Relative_Percent)) ~
pull(filter(diet_and_guidelines, food == "fruits"), sex) +
(1 | pull(filter(diet_and_guidelines, food == "fruits"),
location_name))
REML criterion at convergence: -1245.3
Scaled residuals:
Min 1Q Median 3Q Max
-6.4919 -0.3057 -0.0005 0.2940 6.5088
Random effects:
Groups Name
pull(filter(diet_and_guidelines, food == "fruits"), location_name) (Intercept)
Residual
Variance Std.Dev.
8.212e-02 0.286573
3.222e-05 0.005676
Number of obs: 390, groups:
pull(filter(diet_and_guidelines, food == "fruits"), location_name), 195
Fixed effects:
Estimate
(Intercept) 1.562e+00
pull(filter(diet_and_guidelines, food == "fruits"), sex)Male -5.570e-02
Std. Error
(Intercept) 2.053e-02
pull(filter(diet_and_guidelines, food == "fruits"), sex)Male 5.748e-04
df t value
(Intercept) 1.941e+02 76.12
pull(filter(diet_and_guidelines, food == "fruits"), sex)Male 1.940e+02 -96.89
Pr(>|t|)
(Intercept) <2e-16 ***
pull(filter(diet_and_guidelines, food == "fruits"), sex)Male <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr)
p((__,f=="s -0.014t.test(log10(pull(filter(wide_diet,
food == "fruits"),
Relative_Percent_Female)),
log10(pull(filter(wide_diet,
food == "fruits"),
Relative_Percent_Male)),
var.equal = TRUE, paired = TRUE)
Paired t-test
data: log10(pull(filter(wide_diet, food == "fruits"), Relative_Percent_Female)) and log10(pull(filter(wide_diet, food == "fruits"), Relative_Percent_Male))
t = 96.892, df = 194, p-value < 2.2e-16
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
0.05456356 0.05683104
sample estimates:
mean of the differences
0.0556973 means <- wide_diet %>%
filter(food == "fruits") %>%
summarise(female = mean(log10(Relative_Percent_Female)),
male = mean(log10(Relative_Percent_Male)))
means# A tibble: 1 x 2
female male
<dbl> <dbl>
1 1.56 1.51Notice here that the mean of the differences value is indeed the same as the coefficient and the \(t\)-value and the p-value match. The \(\beta_{1}\) coefficient value (slope) matches the difference between the means value calculated in \(t\)-test. The intercept coefficient \(\beta_{0}\) value of 1.56 (when sex is zero or female) also matches the mean value for the female group when calculated separately. Ideally you would want to check the residual plots of the model, but we will skip this for the sake of space.
Now we are interested in the influence of age group on dietary consumption, because there are 15 age groups we can’t assess the influence of age group on consumption using the \(t\)-test, as this test can only compare 2 groups.
If we wanted to test the hypothesis that there are any age group differences, that at least one of the groups is different from the others; we could use an ANOVA test. This test allows us to compare means of 3 or more groups by evaluating the variance of the data within the groups and among the groups.
Our null hypothesis is that all age groups have equal means: \[ H_0: \mu_{1} = \mu_{2} =\mu_{3}=\mu_{4} = ... \mu_{15} \]
The alternative hypothesis is that at least one age group mean is not equal to the others.
Importantly, if we reject the null, we do not know which group means are different from one another. Subsequent testing is required if we want to know this information. In this case we call this type of non-specific hypothesis an “omnibus” hypothesis.
You could actually perform an ANOVA to compare two means, but in this case you would get an \(F\)-statistic instead of a \(t\)-statistic which would be equivalent to \(t^2\). However it is not conventional to use ANOVA for only 2 means. The \(F\)-statistic is derived form the \(F\)-test is used for a few different type of tests. In the ANOVA the F-test is calculated as:
\[F = \frac{ variability\ between\ the \ groups}{ variablity\ within\ the \ groups}\]
The larger the ratio, the larger the variability between the groups, thus the more likely that the data for each group comes from a different distribution with different means, suggesting that the groups are different.
It turns out that the ANOVA test is also equivalent to a specialized form of regression.
We will demonstrate this by evaluating how the consumption of red meat varies by age group using an ANOVA and a linear regression.
Thinking about how we want to know if red meat consumption differs between age groups from the linear regression perspective, we could also describe our null hypothesis as:
There is no influence of age group identity on consumption or there is no relationship between age group identity and consumption.
And we could describe our alternative hypothesis as:
Age group identity does influence consumption or explain some of the variation in consumption.
OK, so let’s take a look at our data.
First let’s make age_group_name a factor:
The ANOVA assumptions are quite similar to the \(t\)-test assumptions:
So let’s evaluate our assumptions for the groups we are comparing, starting with normality using Q-Q plots. We will assess fruit consumption again.
all_age_diet_and_guidelines %>%
filter(food == "red meat") %>%
ggplot(aes(sample = log10(Relative_Percent))) +
facet_wrap(~ age_group_name) +
geom_qq() +
geom_qq_line()
OK, these look pretty good.
Now let’s look at variance.
The Bartlett’s test works well if the data appears to be quite normally distributed, while the Fligner-Killeen test is nonparametric and does not assume normality of the data.
Another popular test is the Levene’s test, which is more robust to violations of normality than the Bartlett’s test, but not as robust as the Fligner-Killeen test.
All three tests test the null hypothesis that the variances are equal among all of the groups.
Note: Remember that \(\sigma\) is the standard deviation and that \(\sigma^2\) is the variance.
Recall that \(\sigma^2 = \frac{\sum(x_i - \mu)^2}{N}\) when we are thinking of the true populations of the groups.
Where \(N\) is the population size
\(x_i\) are each value from the population
\(\mu\) is the mean of the population
Thus the variance is the average difference of the values in the distribution from the mean of the distribution.
\[ H_0: \sigma_1^2 = \sigma_2^2 = \sigma_3^2 ... = \sigma_n^2 \]
The alternative hypothesis is that at least one of the group \(\sigma_i^2\) is not equal to the other group \(\sigma^2\) values.
\[H_a:\sigma_i^2 \neq \sigma_j^2 \] for at least one pair (\(i\),\(j\))
In our case, the data appears to be quite normally distributed, however we will demonstrate how to perform each of these tests.
The bartlett.test() function of the stats package can be used to perform the Bartlett’s test, the fligner.test() function of the stats package can be used to perform the Fligner-Killeen test and the leveneTest() function of the car package can be used for the Levene’s test.
# Bartlett's Test of Homogeneity of Variances
bartlett.test(
log10(pull(filter(all_age_diet_and_guidelines,
food == "red meat"),
Relative_Percent)) ~
pull(filter(all_age_diet_and_guidelines,
food == "red meat"),
age_group_name))
Bartlett test of homogeneity of variances
data: log10(pull(filter(all_age_diet_and_guidelines, food == "red meat"), Relative_Percent)) by pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name)
Bartlett's K-squared = 11.139, df = 14, p-value = 0.6751# Levene's Test of Homogeneity of Variances
car::leveneTest(
log10(pull(filter(all_age_diet_and_guidelines,
food == "red meat"),
Relative_Percent)) ~
pull(filter(all_age_diet_and_guidelines,
food == "red meat"),
age_group_name)) Levene's Test for Homogeneity of Variance (center = median)
Df F value Pr(>F)
group 14 0.484 0.9429
5835 # Fligner-Killeen Test of Homogeneity of Variances
fligner.test(
log10(pull(filter(all_age_diet_and_guidelines,
food == "red meat"),
Relative_Percent)) ~
pull(filter(all_age_diet_and_guidelines,
food == "red meat"),
age_group_name))
Fligner-Killeen test of homogeneity of variances
data: log10(pull(filter(all_age_diet_and_guidelines, food == "red meat"), Relative_Percent)) by pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name)
Fligner-Killeen:med chi-squared = 7.7956, df = 14, p-value = 0.8997In all cases our data does not appear to violate the Homogeneity of Variances assumption as our p value was > 0.05 for all three tests, thus there is not enough evidence to reject the null.
We will also confirm again that our data is balanced for all groups, meaning we have the same or similar number of samples for each group.
# A tibble: 15 x 2
age_group_name n
<chr> <int>
1 25 to 29 390
2 30 to 34 390
3 35 to 39 390
4 40 to 44 390
5 45 to 49 390
6 50 to 54 390
7 55 to 59 390
8 60 to 64 390
9 65 to 69 390
10 70 to 74 390
11 75 to 79 390
12 80 to 84 390
13 85 to 89 390
14 90 to 94 390
15 95 plus 390Looks good!
Now we will use the aov() function of the stats package to perform the ANOVA test. We will be performing what is called a one-way ANOVA because we only have one dependent/response/outcome variable. We will also perform a linear regression for comparison.
summary(aov(
log10(pull(filter(all_age_diet_and_guidelines,
food == "red meat"),
Relative_Percent)) ~
pull(filter(all_age_diet_and_guidelines,
food == "red meat"),
age_group_name))) Df
pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name) 14
Residuals 5835
Sum Sq
pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name) 47.6
Residuals 870.7
Mean Sq
pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name) 3.403
Residuals 0.149
F value
pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name) 22.8
Residuals
Pr(>F)
pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name) <2e-16
Residuals
pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name) ***
Residuals
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1all_age_diet_and_guidelines %>%
filter(food == "red meat") %>%
lm(log10(Relative_Percent) ~ age_group_name, data= .) %>%
summary()
Call:
lm(formula = log10(Relative_Percent) ~ age_group_name, data = .)
Residuals:
Min 1Q Median 3Q Max
-1.06714 -0.28094 0.00305 0.29301 1.17376
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.994651 0.019561 101.973 < 2e-16 ***
age_group_name30 to 34 -0.003525 0.027663 -0.127 0.898605
age_group_name35 to 39 -0.006997 0.027663 -0.253 0.800322
age_group_name40 to 44 -0.016040 0.027663 -0.580 0.562037
age_group_name45 to 49 -0.032271 0.027663 -1.167 0.243425
age_group_name50 to 54 -0.050053 0.027663 -1.809 0.070442 .
age_group_name55 to 59 -0.069398 0.027663 -2.509 0.012144 *
age_group_name60 to 64 -0.091436 0.027663 -3.305 0.000954 ***
age_group_name65 to 69 -0.116689 0.027663 -4.218 2.50e-05 ***
age_group_name70 to 74 -0.142474 0.027663 -5.150 2.68e-07 ***
age_group_name75 to 79 -0.169369 0.027663 -6.123 9.80e-10 ***
age_group_name80 to 84 -0.228844 0.027663 -8.273 < 2e-16 ***
age_group_name85 to 89 -0.235069 0.027663 -8.498 < 2e-16 ***
age_group_name90 to 94 -0.238725 0.027663 -8.630 < 2e-16 ***
age_group_name95 plus -0.239223 0.027663 -8.648 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.3863 on 5835 degrees of freedom
Multiple R-squared: 0.05188, Adjusted R-squared: 0.0496
F-statistic: 22.8 on 14 and 5835 DF, p-value: < 2.2e-16We can see that the \(F\)-statistic (at the bottom of the lm() output) is the same in both outputs and the p-value for the \(F\)-statistic is the same!
We also see that the degrees of freedom for the \(F\)-statistic is 14. This makes sense because we have 15 different age groups and degrees of freedom for the \(F\)-statistic are calculated as \(df = n - 1\).
So in our case: \(df = 15 -1\).
The difference here is that with the lm() test we also get information about how the individual age groups influence Relative_Percent consumption of fruits. Notice that if we look at all the age groups:
# A tibble: 15 x 1
age_group_name
<chr>
1 25 to 29
2 30 to 34
3 35 to 39
4 40 to 44
5 45 to 49
6 50 to 54
7 55 to 59
8 60 to 64
9 65 to 69
10 70 to 74
11 75 to 79
12 80 to 84
13 85 to 89
14 90 to 94
15 95 plus We can see that our lm() results are missing one of the age groups, the 25 to 29 age group. That is because this is the reference group and the coefficients indicate the slope or difference in Relative_Percent consumption rates for each listed age group compared to the reference group.
Now let’s account for the paired location_name structure within our data as well. To do this we add this as another term in the aov() and the lm() formula with a +. Note that now that we have two categorical independent variables/covariates/predictors. In this case we are comparing both the age groups and the location groups. The anova() function of the stats package will be used with the lm() function to print the resulting \(F\)-statistic, but not the statistics for each of the levels of the age groups and location groups. This function does not actually directly perform ANOVA like the aov() function, but instead prints a variance table using a lm() or glm() object.
summary(aov(
log10(pull(filter(all_age_diet_and_guidelines,
food == "red meat"),
Relative_Percent)) ~
pull(filter(all_age_diet_and_guidelines,
food == "red meat"),
age_group_name) +
pull(filter(all_age_diet_and_guidelines,
food == "red meat"),
location_name))) Df
pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name) 14
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name) 194
Residuals 5641
Sum Sq
pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name) 47.6
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name) 819.7
Residuals 51.0
Mean Sq
pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name) 3.403
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name) 4.225
Residuals 0.009
F value
pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name) 376.1
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name) 467.0
Residuals
Pr(>F)
pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name) <2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name) <2e-16
Residuals
pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name) ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name) ***
Residuals
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(lm(
log10(pull(filter(all_age_diet_and_guidelines,
food == "red meat"),
mean)) ~
pull(filter(all_age_diet_and_guidelines,
food == "red meat"),
age_group_name) +
pull(filter(all_age_diet_and_guidelines,
food == "red meat"),
location_name)))Analysis of Variance Table
Response: log10(pull(filter(all_age_diet_and_guidelines, food == "red meat"),
Response: mean))
Df
pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name) 14
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name) 194
Residuals 5641
Sum Sq
pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name) 47.64
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name) 819.66
Residuals 51.04
Mean Sq
pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name) 3.4029
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name) 4.2250
Residuals 0.0090
F value
pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name) 376.12
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name) 466.99
Residuals
Pr(>F)
pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name) < 2.2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name) < 2.2e-16
Residuals
pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name) ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name) ***
Residuals
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1We can see that indeed the \(F\)-values and p-values match. In this case, this analysis suggests that there is a significant infleunce of age group identity on consumption, even when controlling for location identity and vice versa. The ANOVA results indicate that the means are different across these groups, but it does not inform us about which groups are different. If we instead perform the lm() analysis and use summary() to print the results instead of anova(), we can get more information about possible specific group differences. Recall however, that these are relative to the reference level for the age group and location and that these values are calculated for the effect on consumption while controlling for the other model term.
summary(lm(
log10(pull(filter(all_age_diet_and_guidelines,
food == "red meat"),
mean)) ~
pull(filter(all_age_diet_and_guidelines,
food == "red meat"),
age_group_name) +
pull(filter(all_age_diet_and_guidelines,
food == "red meat"),
location_name)))
Call:
lm(formula = log10(pull(filter(all_age_diet_and_guidelines, food ==
"red meat"), mean)) ~ pull(filter(all_age_diet_and_guidelines,
food == "red meat"), age_group_name) + pull(filter(all_age_diet_and_guidelines,
food == "red meat"), location_name))
Residuals:
Min 1Q Median 3Q Max
-0.21003 -0.08909 -0.00097 0.09118 0.33175
Coefficients:
Estimate
(Intercept) 1.0250799
pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name)30 to 34 -0.0035250
pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name)35 to 39 -0.0069971
pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name)40 to 44 -0.0160403
pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name)45 to 49 -0.0322710
pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name)50 to 54 -0.0500527
pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name)55 to 59 -0.0693983
pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name)60 to 64 -0.0914365
pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name)65 to 69 -0.1166894
pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name)70 to 74 -0.1424738
pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name)75 to 79 -0.1693692
pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name)80 to 84 -0.2288442
pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name)85 to 89 -0.2350692
pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name)90 to 94 -0.2387255
pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name)95 plus -0.2392227
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Albania 0.4696634
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Algeria 0.0524978
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)American Samoa 0.4781072
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Andorra 0.7709817
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Angola 0.2502250
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Antigua and Barbuda 0.3148782
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Argentina 0.8391115
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Armenia 0.4143613
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Australia 0.8382715
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Austria 0.9006351
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Azerbaijan 0.3013522
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Bahrain 0.2892541
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Bangladesh -0.6036909
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Barbados 0.3355809
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Belarus 0.7101162
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Belgium 0.7081735
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Belize 0.1840253
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Benin -0.3251738
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Bermuda 0.4539095
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Bhutan -0.1193613
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Bolivia 0.5011897
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Bosnia and Herzegovina 0.1966458
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Botswana 0.1360033
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Brazil 0.7420931
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Brunei 0.2248900
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Bulgaria 0.7016126
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Burkina Faso 0.0952272
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Burundi -0.1975274
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Cambodia 0.7246230
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Cameroon -0.0547177
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Canada 0.6965652
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Cape Verde 0.4033481
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Central African Republic 0.3695721
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Chad 0.0445016
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Chile 0.6899420
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)China 0.9036505
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Colombia 0.3677083
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Comoros -0.1092061
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Congo -0.2562887
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Costa Rica 0.3922246
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Cote d'Ivoire -0.3878599
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Croatia 0.6168738
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Cuba 0.3715729
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Cyprus 0.6210060
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Czech Republic 0.6754221
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Democratic Republic of the Congo -0.1381953
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Denmark 0.6940034
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Djibouti 0.2040664
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Dominica 0.2201500
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Dominican Republic 0.2904822
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Ecuador 0.5001219
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Egypt 0.1407597
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)El Salvador 0.0564578
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Equatorial Guinea 0.4145078
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Eritrea -0.1427694
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Estonia 0.5724487
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Ethiopia -0.1749948
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Federated States of Micronesia 0.2177319
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Fiji 0.3063835
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Finland 0.6616460
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)France 0.7352843
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Gabon 0.1926532
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Georgia 0.2530224
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Germany 0.7386742
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Ghana -0.3464979
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Greece 0.7022224
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Greenland 0.7880282
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Grenada 0.2394215
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Guam 0.5937555
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Guatemala 0.0139062
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Guinea -0.1905498
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Guinea-Bissau 0.1322312
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Guyana -0.2063983
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Haiti -0.0491435
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Honduras 0.1244476
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Hungary 0.6410019
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Iceland 0.6839722
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)India -0.6388459
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Indonesia 0.9884246
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Iran -0.0399115
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Iraq -0.3147627
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Ireland 0.8205275
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Israel 0.5031271
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Italy 0.7012419
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Jamaica -0.0196064
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Japan 0.4163050
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Jordan 0.1122555
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Kazakhstan 0.6667740
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Kenya 0.1062621
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Kiribati 0.1360152
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Kuwait 0.3628791
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Kyrgyzstan 0.4299491
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Laos 1.2383820
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Latvia 0.6051096
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Lebanon 0.3358240
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Lesotho 0.0827870
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Liberia -0.3810751
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Libya 0.1058210
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Lithuania 0.6853303
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Luxembourg 0.8252128
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Macedonia 0.3375009
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Madagascar 0.0086607
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Malawi -0.2390062
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Malaysia 0.9037008
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Maldives 0.9341352
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Mali 0.2545834
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Malta 0.6861462
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Marshall Islands 0.2453513
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Mauritania 0.2586036
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Mauritius 0.1018209
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Mexico 0.5019364
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Moldova 0.2694136
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Mongolia 0.8050147
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Montenegro 0.7197936
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Morocco 0.0536665
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Mozambique -0.1454548
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Myanmar 0.9679235
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Namibia 0.2766865
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Nepal 0.0040043
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Netherlands 0.7182009
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)New Zealand 0.8215769
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Nicaragua -0.1022029
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Niger 0.1929072
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Nigeria -0.1687456
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)North Korea 0.9568532
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Northern Mariana Islands 0.4519371
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Norway 0.6802116
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Oman 0.3715916
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Pakistan 0.0163832
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Palestine -0.0001115
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Panama 0.4910430
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Papua New Guinea 0.1780454
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Paraguay 0.6105228
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Peru -0.0270767
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Philippines 0.9642365
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Poland 0.7119822
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Portugal 0.7651172
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Puerto Rico 0.4442532
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Qatar 0.4226739
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Romania 0.6732206
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Russian Federation 0.5601442
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Rwanda -0.2771514
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Saint Lucia 0.2295368
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Saint Vincent and the Grenadines 0.2179043
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Samoa 0.2797823
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Sao Tome and Principe -0.0434018
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Saudi Arabia 0.0524593
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Senegal -0.0681700
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Serbia 0.5359842
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Seychelles 0.3146719
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Sierra Leone -0.5214405
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Singapore 0.5946425
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Slovakia 0.6438632
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Slovenia 0.7116904
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Solomon Islands -0.0090348
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Somalia -0.2564213
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)South Africa 0.4267033
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)South Korea 0.5949378
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)South Sudan 0.0164738
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Spain 0.7138244
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Sri Lanka 0.8976439
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Sudan -0.0465153
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Suriname 0.1906880
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Swaziland 0.3040096
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Sweden 0.7247779
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Switzerland 0.7281804
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Syria -0.0024640
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Taiwan 0.9249290
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Tajikistan 0.0176046
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Tanzania -0.1278459
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Thailand 0.8582708
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)The Bahamas 0.5890517
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)The Gambia -0.3863879
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Timor-Leste 1.2355139
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Togo -0.3083612
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Tonga 0.3002675
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Trinidad and Tobago 0.1770619
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Tunisia 0.0071742
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Turkey 0.0631309
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Turkmenistan 0.7290156
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Uganda 0.0146972
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Ukraine 0.3932282
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)United Arab Emirates 0.2716685
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)United Kingdom 0.5932920
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)United States 0.7390080
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Uruguay 0.6597541
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Uzbekistan 0.4620568
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Vanuatu 0.1977037
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Venezuela 0.4446078
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Vietnam 0.5087180
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Virgin Islands, U.S. 0.4675163
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Yemen -0.1277139
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Zambia -0.1062726
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Zimbabwe 0.0462498
Std. Error
(Intercept) 0.0179787
pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name)30 to 34 0.0068116
pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name)35 to 39 0.0068116
pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name)40 to 44 0.0068116
pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name)45 to 49 0.0068116
pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name)50 to 54 0.0068116
pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name)55 to 59 0.0068116
pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name)60 to 64 0.0068116
pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name)65 to 69 0.0068116
pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name)70 to 74 0.0068116
pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name)75 to 79 0.0068116
pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name)80 to 84 0.0068116
pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name)85 to 89 0.0068116
pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name)90 to 94 0.0068116
pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name)95 plus 0.0068116
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Albania 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Algeria 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)American Samoa 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Andorra 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Angola 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Antigua and Barbuda 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Argentina 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Armenia 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Australia 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Austria 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Azerbaijan 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Bahrain 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Bangladesh 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Barbados 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Belarus 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Belgium 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Belize 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Benin 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Bermuda 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Bhutan 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Bolivia 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Bosnia and Herzegovina 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Botswana 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Brazil 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Brunei 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Bulgaria 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Burkina Faso 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Burundi 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Cambodia 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Cameroon 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Canada 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Cape Verde 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Central African Republic 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Chad 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Chile 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)China 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Colombia 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Comoros 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Congo 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Costa Rica 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Cote d'Ivoire 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Croatia 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Cuba 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Cyprus 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Czech Republic 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Democratic Republic of the Congo 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Denmark 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Djibouti 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Dominica 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Dominican Republic 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Ecuador 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Egypt 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)El Salvador 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Equatorial Guinea 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Eritrea 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Estonia 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Ethiopia 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Federated States of Micronesia 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Fiji 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Finland 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)France 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Gabon 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Georgia 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Germany 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Ghana 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Greece 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Greenland 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Grenada 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Guam 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Guatemala 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Guinea 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Guinea-Bissau 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Guyana 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Haiti 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Honduras 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Hungary 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Iceland 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)India 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Indonesia 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Iran 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Iraq 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Ireland 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Israel 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Italy 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Jamaica 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Japan 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Jordan 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Kazakhstan 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Kenya 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Kiribati 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Kuwait 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Kyrgyzstan 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Laos 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Latvia 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Lebanon 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Lesotho 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Liberia 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Libya 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Lithuania 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Luxembourg 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Macedonia 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Madagascar 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Malawi 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Malaysia 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Maldives 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Mali 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Malta 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Marshall Islands 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Mauritania 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Mauritius 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Mexico 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Moldova 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Mongolia 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Montenegro 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Morocco 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Mozambique 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Myanmar 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Namibia 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Nepal 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Netherlands 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)New Zealand 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Nicaragua 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Niger 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Nigeria 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)North Korea 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Northern Mariana Islands 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Norway 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Oman 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Pakistan 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Palestine 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Panama 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Papua New Guinea 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Paraguay 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Peru 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Philippines 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Poland 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Portugal 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Puerto Rico 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Qatar 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Romania 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Russian Federation 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Rwanda 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Saint Lucia 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Saint Vincent and the Grenadines 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Samoa 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Sao Tome and Principe 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Saudi Arabia 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Senegal 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Serbia 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Seychelles 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Sierra Leone 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Singapore 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Slovakia 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Slovenia 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Solomon Islands 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Somalia 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)South Africa 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)South Korea 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)South Sudan 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Spain 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Sri Lanka 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Sudan 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Suriname 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Swaziland 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Sweden 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Switzerland 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Syria 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Taiwan 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Tajikistan 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Tanzania 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Thailand 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)The Bahamas 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)The Gambia 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Timor-Leste 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Togo 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Tonga 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Trinidad and Tobago 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Tunisia 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Turkey 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Turkmenistan 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Uganda 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Ukraine 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)United Arab Emirates 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)United Kingdom 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)United States 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Uruguay 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Uzbekistan 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Vanuatu 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Venezuela 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Vietnam 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Virgin Islands, U.S. 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Yemen 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Zambia 0.0245594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Zimbabwe 0.0245594
t value
(Intercept) 57.016
pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name)30 to 34 -0.518
pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name)35 to 39 -1.027
pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name)40 to 44 -2.355
pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name)45 to 49 -4.738
pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name)50 to 54 -7.348
pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name)55 to 59 -10.188
pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name)60 to 64 -13.424
pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name)65 to 69 -17.131
pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name)70 to 74 -20.916
pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name)75 to 79 -24.865
pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name)80 to 84 -33.596
pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name)85 to 89 -34.510
pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name)90 to 94 -35.047
pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name)95 plus -35.120
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Albania 19.124
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Algeria 2.138
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)American Samoa 19.467
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Andorra 31.392
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Angola 10.189
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Antigua and Barbuda 12.821
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Argentina 34.167
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Armenia 16.872
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Australia 34.132
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Austria 36.672
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Azerbaijan 12.270
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Bahrain 11.778
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Bangladesh -24.581
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Barbados 13.664
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Belarus 28.914
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Belgium 28.835
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Belize 7.493
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Benin -13.240
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Bermuda 18.482
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Bhutan -4.860
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Bolivia 20.407
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Bosnia and Herzegovina 8.007
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Botswana 5.538
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Brazil 30.216
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Brunei 9.157
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Bulgaria 28.568
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Burkina Faso 3.877
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Burundi -8.043
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Cambodia 29.505
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Cameroon -2.228
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Canada 28.362
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Cape Verde 16.423
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Central African Republic 15.048
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Chad 1.812
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Chile 28.093
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)China 36.794
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Colombia 14.972
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Comoros -4.447
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Congo -10.435
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Costa Rica 15.970
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Cote d'Ivoire -15.793
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Croatia 25.118
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Cuba 15.130
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Cyprus 25.286
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Czech Republic 27.502
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Democratic Republic of the Congo -5.627
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Denmark 28.258
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Djibouti 8.309
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Dominica 8.964
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Dominican Republic 11.828
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Ecuador 20.364
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Egypt 5.731
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)El Salvador 2.299
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Equatorial Guinea 16.878
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Eritrea -5.813
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Estonia 23.309
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Ethiopia -7.125
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Federated States of Micronesia 8.866
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Fiji 12.475
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Finland 26.941
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)France 29.939
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Gabon 7.844
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Georgia 10.302
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Germany 30.077
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Ghana -14.109
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Greece 28.593
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Greenland 32.087
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Grenada 9.749
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Guam 24.176
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Guatemala 0.566
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Guinea -7.759
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Guinea-Bissau 5.384
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Guyana -8.404
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Haiti -2.001
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Honduras 5.067
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Hungary 26.100
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Iceland 27.850
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)India -26.012
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Indonesia 40.246
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Iran -1.625
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Iraq -12.816
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Ireland 33.410
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Israel 20.486
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Italy 28.553
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Jamaica -0.798
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Japan 16.951
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Jordan 4.571
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Kazakhstan 27.149
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Kenya 4.327
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Kiribati 5.538
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Kuwait 14.776
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Kyrgyzstan 17.506
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Laos 50.424
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Latvia 24.639
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Lebanon 13.674
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Lesotho 3.371
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Liberia -15.516
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Libya 4.309
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Lithuania 27.905
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Luxembourg 33.601
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Macedonia 13.742
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Madagascar 0.353
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Malawi -9.732
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Malaysia 36.796
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Maldives 38.036
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Mali 10.366
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Malta 27.938
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Marshall Islands 9.990
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Mauritania 10.530
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Mauritius 4.146
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Mexico 20.438
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Moldova 10.970
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Mongolia 32.778
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Montenegro 29.308
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Morocco 2.185
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Mozambique -5.923
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Myanmar 39.411
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Namibia 11.266
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Nepal 0.163
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Netherlands 29.243
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)New Zealand 33.453
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Nicaragua -4.161
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Niger 7.855
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Nigeria -6.871
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)North Korea 38.961
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Northern Mariana Islands 18.402
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Norway 27.697
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Oman 15.130
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Pakistan 0.667
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Palestine -0.005
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Panama 19.994
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Papua New Guinea 7.250
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Paraguay 24.859
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Peru -1.102
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Philippines 39.261
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Poland 28.990
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Portugal 31.154
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Puerto Rico 18.089
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Qatar 17.210
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Romania 27.412
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Russian Federation 22.808
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Rwanda -11.285
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Saint Lucia 9.346
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Saint Vincent and the Grenadines 8.873
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Samoa 11.392
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Sao Tome and Principe -1.767
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Saudi Arabia 2.136
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Senegal -2.776
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Serbia 21.824
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Seychelles 12.813
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Sierra Leone -21.232
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Singapore 24.212
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Slovakia 26.217
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Slovenia 28.978
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Solomon Islands -0.368
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Somalia -10.441
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)South Africa 17.374
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)South Korea 24.224
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)South Sudan 0.671
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Spain 29.065
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Sri Lanka 36.550
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Sudan -1.894
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Suriname 7.764
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Swaziland 12.379
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Sweden 29.511
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Switzerland 29.650
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Syria -0.100
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Taiwan 37.661
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Tajikistan 0.717
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Tanzania -5.206
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Thailand 34.947
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)The Bahamas 23.985
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)The Gambia -15.733
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Timor-Leste 50.307
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Togo -12.556
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Tonga 12.226
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Trinidad and Tobago 7.210
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Tunisia 0.292
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Turkey 2.571
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Turkmenistan 29.684
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Uganda 0.598
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Ukraine 16.011
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)United Arab Emirates 11.062
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)United Kingdom 24.157
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)United States 30.091
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Uruguay 26.864
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Uzbekistan 18.814
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Vanuatu 8.050
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Venezuela 18.103
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Vietnam 20.714
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Virgin Islands, U.S. 19.036
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Yemen -5.200
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Zambia -4.327
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Zimbabwe 1.883
Pr(>|t|)
(Intercept) < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name)30 to 34 0.604822
pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name)35 to 39 0.304354
pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name)40 to 44 0.018564
pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name)45 to 49 2.21e-06
pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name)50 to 54 2.30e-13
pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name)55 to 59 < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name)60 to 64 < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name)65 to 69 < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name)70 to 74 < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name)75 to 79 < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name)80 to 84 < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name)85 to 89 < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name)90 to 94 < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name)95 plus < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Albania < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Algeria 0.032594
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)American Samoa < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Andorra < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Angola < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Antigua and Barbuda < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Argentina < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Armenia < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Australia < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Austria < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Azerbaijan < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Bahrain < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Bangladesh < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Barbados < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Belarus < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Belgium < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Belize 7.77e-14
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Benin < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Bermuda < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Bhutan 1.21e-06
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Bolivia < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Bosnia and Herzegovina 1.42e-15
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Botswana 3.20e-08
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Brazil < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Brunei < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Bulgaria < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Burkina Faso 0.000107
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Burundi 1.06e-15
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Cambodia < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Cameroon 0.025922
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Canada < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Cape Verde < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Central African Republic < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Chad 0.070040
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Chile < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)China < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Colombia < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Comoros 8.89e-06
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Congo < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Costa Rica < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Cote d'Ivoire < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Croatia < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Cuba < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Cyprus < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Czech Republic < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Democratic Republic of the Congo 1.92e-08
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Denmark < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Djibouti < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Dominica < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Dominican Republic < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Ecuador < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Egypt 1.05e-08
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)El Salvador 0.021551
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Equatorial Guinea < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Eritrea 6.46e-09
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Estonia < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Ethiopia 1.17e-12
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Federated States of Micronesia < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Fiji < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Finland < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)France < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Gabon 5.17e-15
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Georgia < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Germany < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Ghana < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Greece < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Greenland < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Grenada < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Guam < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Guatemala 0.571262
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Guinea 1.01e-14
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Guinea-Bissau 7.57e-08
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Guyana < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Haiti 0.045440
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Honduras 4.17e-07
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Hungary < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Iceland < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)India < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Indonesia < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Iran 0.104197
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Iraq < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Ireland < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Israel < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Italy < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Jamaica 0.424716
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Japan < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Jordan 4.96e-06
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Kazakhstan < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Kenya 1.54e-05
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Kiribati 3.19e-08
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Kuwait < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Kyrgyzstan < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Laos < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Latvia < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Lebanon < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Lesotho 0.000754
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Liberia < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Libya 1.67e-05
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Lithuania < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Luxembourg < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Macedonia < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Madagascar 0.724371
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Malawi < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Malaysia < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Maldives < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Mali < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Malta < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Marshall Islands < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Mauritania < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Mauritius 3.43e-05
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Mexico < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Moldova < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Mongolia < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Montenegro < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Morocco 0.028918
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Mozambique 3.36e-09
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Myanmar < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Namibia < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Nepal 0.870489
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Netherlands < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)New Zealand < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Nicaragua 3.21e-05
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Niger 4.76e-15
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Nigeria 7.07e-12
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)North Korea < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Northern Mariana Islands < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Norway < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Oman < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Pakistan 0.504747
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Palestine 0.996377
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Panama < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Papua New Guinea 4.74e-13
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Paraguay < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Peru 0.270293
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Philippines < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Poland < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Portugal < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Puerto Rico < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Qatar < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Romania < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Russian Federation < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Rwanda < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Saint Lucia < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Saint Vincent and the Grenadines < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Samoa < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Sao Tome and Principe 0.077246
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Saudi Arabia 0.032721
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Senegal 0.005526
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Serbia < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Seychelles < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Sierra Leone < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Singapore < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Slovakia < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Slovenia < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Solomon Islands 0.712979
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Somalia < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)South Africa < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)South Korea < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)South Sudan 0.502392
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Spain < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Sri Lanka < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Sudan 0.058278
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Suriname 9.68e-15
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Swaziland < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Sweden < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Switzerland < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Syria 0.920087
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Taiwan < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Tajikistan 0.473518
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Tanzania 2.00e-07
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Thailand < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)The Bahamas < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)The Gambia < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Timor-Leste < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Togo < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Tonga < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Trinidad and Tobago 6.35e-13
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Tunisia 0.770207
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Turkey 0.010180
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Turkmenistan < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Uganda 0.549574
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Ukraine < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)United Arab Emirates < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)United Kingdom < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)United States < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Uruguay < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Uzbekistan < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Vanuatu 1.00e-15
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Venezuela < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Vietnam < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Virgin Islands, U.S. < 2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Yemen 2.06e-07
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Zambia 1.54e-05
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Zimbabwe 0.059728
(Intercept) ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name)30 to 34
pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name)35 to 39
pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name)40 to 44 *
pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name)45 to 49 ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name)50 to 54 ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name)55 to 59 ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name)60 to 64 ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name)65 to 69 ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name)70 to 74 ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name)75 to 79 ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name)80 to 84 ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name)85 to 89 ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name)90 to 94 ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name)95 plus ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Albania ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Algeria *
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)American Samoa ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Andorra ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Angola ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Antigua and Barbuda ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Argentina ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Armenia ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Australia ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Austria ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Azerbaijan ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Bahrain ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Bangladesh ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Barbados ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Belarus ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Belgium ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Belize ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Benin ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Bermuda ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Bhutan ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Bolivia ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Bosnia and Herzegovina ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Botswana ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Brazil ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Brunei ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Bulgaria ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Burkina Faso ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Burundi ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Cambodia ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Cameroon *
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Canada ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Cape Verde ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Central African Republic ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Chad .
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Chile ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)China ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Colombia ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Comoros ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Congo ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Costa Rica ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Cote d'Ivoire ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Croatia ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Cuba ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Cyprus ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Czech Republic ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Democratic Republic of the Congo ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Denmark ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Djibouti ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Dominica ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Dominican Republic ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Ecuador ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Egypt ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)El Salvador *
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Equatorial Guinea ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Eritrea ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Estonia ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Ethiopia ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Federated States of Micronesia ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Fiji ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Finland ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)France ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Gabon ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Georgia ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Germany ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Ghana ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Greece ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Greenland ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Grenada ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Guam ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Guatemala
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Guinea ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Guinea-Bissau ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Guyana ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Haiti *
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Honduras ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Hungary ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Iceland ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)India ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Indonesia ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Iran
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Iraq ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Ireland ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Israel ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Italy ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Jamaica
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Japan ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Jordan ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Kazakhstan ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Kenya ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Kiribati ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Kuwait ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Kyrgyzstan ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Laos ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Latvia ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Lebanon ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Lesotho ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Liberia ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Libya ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Lithuania ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Luxembourg ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Macedonia ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Madagascar
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Malawi ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Malaysia ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Maldives ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Mali ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Malta ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Marshall Islands ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Mauritania ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Mauritius ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Mexico ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Moldova ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Mongolia ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Montenegro ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Morocco *
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Mozambique ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Myanmar ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Namibia ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Nepal
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Netherlands ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)New Zealand ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Nicaragua ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Niger ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Nigeria ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)North Korea ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Northern Mariana Islands ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Norway ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Oman ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Pakistan
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Palestine
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Panama ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Papua New Guinea ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Paraguay ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Peru
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Philippines ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Poland ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Portugal ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Puerto Rico ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Qatar ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Romania ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Russian Federation ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Rwanda ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Saint Lucia ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Saint Vincent and the Grenadines ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Samoa ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Sao Tome and Principe .
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Saudi Arabia *
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Senegal **
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Serbia ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Seychelles ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Sierra Leone ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Singapore ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Slovakia ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Slovenia ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Solomon Islands
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Somalia ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)South Africa ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)South Korea ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)South Sudan
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Spain ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Sri Lanka ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Sudan .
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Suriname ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Swaziland ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Sweden ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Switzerland ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Syria
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Taiwan ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Tajikistan
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Tanzania ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Thailand ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)The Bahamas ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)The Gambia ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Timor-Leste ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Togo ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Tonga ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Trinidad and Tobago ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Tunisia
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Turkey *
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Turkmenistan ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Uganda
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Ukraine ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)United Arab Emirates ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)United Kingdom ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)United States ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Uruguay ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Uzbekistan ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Vanuatu ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Venezuela ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Vietnam ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Virgin Islands, U.S. ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Yemen ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Zambia ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)Zimbabwe .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.09512 on 5641 degrees of freedom
Multiple R-squared: 0.9444, Adjusted R-squared: 0.9424
F-statistic: 460.9 on 208 and 5641 DF, p-value: < 2.2e-16We could instead perform a similar analysis as we did for the two group analysis where we controlled for the paired data structure using a random effect based on location. This is how we would could account for this data structure within the aov() function using Error() and again using the lmer() function with 1|variable_name and the anova() function to print the results.
summary(aov(
log10(pull(filter(all_age_diet_and_guidelines,
food == "red meat"),
Relative_Percent)) ~
pull(filter(all_age_diet_and_guidelines,
food == "red meat"),
age_group_name) +
Error(pull(filter(all_age_diet_and_guidelines,
food == "red meat"),
location_name))))
Error: pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name)
Df Sum Sq Mean Sq F value Pr(>F)
Residuals 194 819.7 4.225
Error: Within
Df
pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name) 14
Residuals 5641
Sum Sq
pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name) 47.64
Residuals 51.04
Mean Sq
pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name) 3.403
Residuals 0.009
F value
pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name) 376.1
Residuals
Pr(>F)
pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name) <2e-16
Residuals
pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name) ***
Residuals
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(lmer(
log10(pull(filter(all_age_diet_and_guidelines,
food == "red meat"),
Relative_Percent)) ~
pull(filter(all_age_diet_and_guidelines,
food == "red meat"),
age_group_name) +
(1|pull(filter(all_age_diet_and_guidelines,
food == "red meat"),
location_name))))Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq
pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name) 47.641
Mean Sq
pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name) 3.4029
NumDF
pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name) 14
DenDF
pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name) 5641
F value
pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name) 376.12
Pr(>F)
pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name) < 2.2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name) ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Notice now the results only show for the age_group_name, but they are the same as in the analysis with the fixed effect for location_name.
Now that we know that linear regressions allow us to perform group comparisons like \(t\)-tests and ANOVAs, and can also provide is with additional information, we can extend our analysis a bit further. We could include sex, location_name, and age_group_name in the same linear model and get information about how each of these factors influences dietary consumption, while accounting for the other factors.
anova(lm(
log10(pull(filter(all_age_diet_and_guidelines,
food == "red meat"),
mean)) ~
pull(filter(all_age_diet_and_guidelines,
food == "red meat"),
age_group_name) +
pull(filter(all_age_diet_and_guidelines,
food == "red meat"),
location_name) +
pull(filter(all_age_diet_and_guidelines,
food == "red meat"),
sex)))Analysis of Variance Table
Response: log10(pull(filter(all_age_diet_and_guidelines, food == "red meat"),
Response: mean))
Df
pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name) 14
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name) 194
pull(filter(all_age_diet_and_guidelines, food == "red meat"), sex) 1
Residuals 5640
Sum Sq
pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name) 47.64
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name) 819.66
pull(filter(all_age_diet_and_guidelines, food == "red meat"), sex) 46.66
Residuals 4.37
Mean Sq
pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name) 3.403
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name) 4.225
pull(filter(all_age_diet_and_guidelines, food == "red meat"), sex) 46.663
Residuals 0.001
F value
pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name) 4388.0
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name) 5448.1
pull(filter(all_age_diet_and_guidelines, food == "red meat"), sex) 60171.4
Residuals
Pr(>F)
pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name) < 2.2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name) < 2.2e-16
pull(filter(all_age_diet_and_guidelines, food == "red meat"), sex) < 2.2e-16
Residuals
pull(filter(all_age_diet_and_guidelines, food == "red meat"), age_group_name) ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), location_name) ***
pull(filter(all_age_diet_and_guidelines, food == "red meat"), sex) ***
Residuals
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1We can see that sex, location_name, and age_group_name all individually significantly influence the consumption of red meat, even when controlling for the other two predictor variables.
Now that we have statistically analyzed the consumption of red meat based on the location, sex, and age group of different populations around the world. Let’s make some visualizations to help with our interpretations.
Let’s try to make a plot that shows the relationship of age group, sex, and location on consumption of red meat.
First we will filter our data for only the data associated with red meat, and then we will create a boxplot graph with age group as the x axis, but include box plots for each sex for each age group. We can include an additional subplot to just look at the relationship of sex and consumption. Recall that the ggplot2 package is very useful for making figures and uses a layering structure to make plots using the + between layers.
all_age_diet_and_guidelines %>%
filter(food == "red meat") %>%
ggplot(aes(x = age_group_name,
y = Relative_Percent,
col = sex)) +
geom_boxplot() +
# this adds the individual points for the sex comparison
geom_jitter(aes(x = sex,
y = Relative_Percent),
# width specifies how wide the points will be plotted
width = .2,
size = 2,
shape = 21) +
# this angles the x axis text and removes the legend
theme(axis.text.x = element_text(angle = 70,
hjust = 1),
legend.position = "none")
OK, this is pretty good, but we can do better.
Let’s try specifically looking at the countries that over-consumed red meat. We can look at these countries by filtering our data where Relative_Percent was greater than 100%. Now we will overlap the jitter points and the boxplot using the position_jitterdoge() as the position in geom_pont(). In order to not obscure our boxplots, we can use the argument alpha to make our jitter points more transparent.
all_age_diet_and_guidelines %>%
filter(food == "red meat") %>%
filter(Relative_Percent > 100) %>%
ggplot(aes(y = Relative_Percent,
x = age_group_name,
fill = sex)) +
# this position option will separate the points by sex
# this is determined by the fill argument in the ggplot() function
# could also use col argument but it would change the style a bit
geom_point(position = position_jitterdodge(),
aes(col = sex),
alpha = 3/10) +
geom_boxplot(outlier.shape=NA) +
theme(axis.text.x = element_text(angle = 70,
hjust = 1))
What are the countries that have such high consumption rates?
# A tibble: 8 x 20
year_id location_name food age_group_id age_group_name sex parameter mean
<dbl> <chr> <chr> <dbl> <chr> <chr> <chr> <dbl>
1 2017 Laos red … 10 25 to 29 Male continuo… 339.
2 2017 Laos red … 11 30 to 34 Male continuo… 292.
3 2017 Laos red … 12 35 to 39 Male continuo… 256.
4 2017 Laos red … 13 40 to 44 Male continuo… 234.
5 2017 Timor-Leste red … 10 25 to 29 Male continuo… 336.
6 2017 Timor-Leste red … 11 30 to 34 Male continuo… 290.
7 2017 Timor-Leste red … 12 35 to 39 Male continuo… 254.
8 2017 Timor-Leste red … 13 40 to 44 Male continuo… 232.
# … with 12 more variables: upper <dbl>, lower <dbl>, unit <chr>,
# direction <chr>, lower_optimal <dbl>, optimal <dbl>, upper_optimal <dbl>,
# unit_optimal <chr>, Relative_Percent <dbl>, range_percent <dbl>,
# percent_over_under <dbl>, opt_achieved <chr>Looks like the males in Laos and Timor_Leste have the highest consumption.
Now let’s plot just the populations that eat less than the optimal amount by filtering for Relative_Percent < 100%.
all_age_diet_and_guidelines %>%
filter(food == "red meat") %>%
filter(Relative_Percent < 100) %>%
ggplot(aes(y = Relative_Percent,
x = age_group_name,
fill = sex)) +
geom_point(position = position_jitterdodge(),
aes(col = sex),
alpha = 3/10) +
geom_boxplot(outlier.shape=NA) +
theme(axis.text.x = element_text(angle = 70,
hjust = 1))
Nice! It would be nice to be able to know what countries each data point corresponds to. One way to do this is using a package called ggiraph. This package is really helpful for creating interactive graphs.
We will use the geom_point_interactive() function to allow us to hover over points to display the country name. We indicate what label we want with the tooltip argument. This function is similar to the normal geom_point() function. Thus, we will include the same arguments as before. However, we will also split the male and female data using facet_wrap() to make things a bit less overwhelming.
Notice that we are creating a plot object before we use the geom_point_interactive().
We are also rendering the plot with the girafe() function of the ggiraph package.
g <- all_age_diet_and_guidelines %>%
filter(food == "red meat") %>%
filter(Relative_Percent < 100) %>%
ggplot(aes(y = Relative_Percent ,
x = age_group_name,
fill = sex)) +
geom_boxplot(outlier.shape = NA) +
facet_wrap(~sex) +
theme(axis.text.x = element_text(angle = 70,
hjust = 1))
g <- g + geom_point_interactive(aes(
color = sex,
tooltip = location_name),
size = 2,
position = position_jitterdodge(),
alpha = 3/10)
girafe(code = print(g))Cool!
From this plot we can see the countries with populations that do well by not over-consuming red meat, (as over-consumption is associated with health risk). We see that different countries greatly vary, we can see that overall younger populations appear to consume more red meat, and men appear to consume red meat.
Let’s do the same thing for the over-consuming countries. We can also take this one step further to show all the points for the same country when we hover over one data point by using the data_id argument of the geom_point_interactive() function.
We can also add links to Wikipedia pages for these countries using the onclick argument. See this link for more information on using the ggirpah package. We will use the base sprintf() function to format our urls for the Wikipedia links into C style to open a new tab for the link when a user clicks on the figure.
all_age_diet_and_guidelines %<>%
mutate(link = sprintf("window.open(\"%s%s\")",
"http://en.wikipedia.org/wiki/",
as.character(pull(all_age_diet_and_guidelines,
location_name))))
g <- all_age_diet_and_guidelines %>%
filter(food == "red meat") %>%
filter(Relative_Percent > 100) %>%
ggplot(aes(y = Relative_Percent ,
x = age_group_name,
fill = sex)) +
geom_boxplot(outlier.shape=NA) +
facet_wrap(~sex) +
theme(legend.position = "none",
axis.text.x = element_text(angle = 70,
hjust = 1)) +
expand_limits(y = 99)
g <- g + geom_point_interactive(aes(
color = sex,
tooltip = location_name,
data_id = location_name,
onclick = link),
size = 2,
position = position_jitterdodge(),
alpha = 3/10)
g <- g + geom_point_interactive(data =
all_age_diet_and_guidelines %>%
filter(food == "red meat") %>%
filter(Relative_Percent > 100) %>%
filter(location_name == "United States"),
aes(fill = location_name,
tooltip = location_name,
data_id = location_name,
onclick = link),
size = 4,
position = position_jitterdodge(),
alpha = 5/10,
color = "black")
girafe(code = print(g))How do different age groups look:
all_age_diet_and_guidelines %>%
filter(food == "red meat") %>%
ggplot(aes(y = Relative_Percent ,
x = sex,
fill = age_group_name)) +
geom_boxplot() +
theme(axis.text.x = element_text(angle = 70,
hjust = 1))
Now let’s take a look at the US data specifically.
# A tibble: 2 x 2
opt_achieved n
<chr> <int>
1 No 27
2 Yes 3OK, it looks like optimal consumption levels were achieved for only 10% of the dietary factors.
Let’s visualize this with a plot:
diet_and_guidelines %>%
filter(location_name == "United States") %>%
ggplot(aes(opt_achieved )) +
geom_bar(position = "dodge")
Let’s look at Females and Males separately:
diet_and_guidelines %>%
filter(sex == "Female",
location_name == "United States") %>%
count(opt_achieved, food)# A tibble: 15 x 3
opt_achieved food n
<chr> <chr> <int>
1 No calcium 1
2 No fiber 1
3 No fruits 1
4 No legumes 1
5 No milk 1
6 No nuts and seeds 1
7 No polyunsaturated fatty acids 1
8 No processed meat 1
9 No red meat 1
10 No seafood omega-3 fatty acids 1
11 No sugar-sweetened beverages 1
12 No vegetables 1
13 No whole grains 1
14 Yes sodium 1
15 Yes trans fatty acids 1diet_and_guidelines %>%
filter(sex == "Male",
location_name == "United States") %>%
count(opt_achieved, food)# A tibble: 15 x 3
opt_achieved food n
<chr> <chr> <int>
1 No calcium 1
2 No fiber 1
3 No fruits 1
4 No legumes 1
5 No milk 1
6 No nuts and seeds 1
7 No polyunsaturated fatty acids 1
8 No processed meat 1
9 No red meat 1
10 No seafood omega-3 fatty acids 1
11 No sodium 1
12 No sugar-sweetened beverages 1
13 No vegetables 1
14 No whole grains 1
15 Yes trans fatty acids 1OK, so females are a bit better about not over-consuming sodium in the United States relative to males.
Both groups are doing well with avoiding trans fatty acids.
Let’s visualize this:
diet_and_guidelines %>%
filter(location_name == "United States") %>%
ggplot(aes(opt_achieved , fill = sex)) +
geom_bar(position = "dodge")
all_age_diet_and_guidelines %>%
filter(location_name == "United States") %>%
ggplot(aes(y = Relative_Percent ,
x = food,
fill = sex)) +
theme(axis.text.x = element_text(angle = 70,
hjust = 1)) +
facet_wrap(~direction, scales = "free") +
geom_boxplot() +
geom_point(position = position_jitterdodge(),
alpha = 3/10)
OK, so we can indeed see that overall consumption of sodium and trans fatty acids is pretty close to optimal. So that’s great. However, Both males and females are over-consuming processed meat, red meat, and sugar-sweetened beverages. On the other hand both genders are not getting adequate intake of all the other dietary factors for optimal health. The population in the United states has especially poor intake of polyunsaturated fats. it also looks like in most cases females are getting less of the dietary factors that pose health risks when under-consumed, with the exception of fruits.
How about if we look at age groups. First let’s see how often the optimal amount was achieved:
all_age_diet_and_guidelines %>%
filter(location_name == "United States",
direction == "high") %>%
count(opt_achieved)# A tibble: 2 x 2
opt_achieved n
<chr> <int>
1 No 103
2 Yes 47all_age_diet_and_guidelines%>%
filter(sex == "Male",
location_name == "United States") %>%
count(opt_achieved, food)# A tibble: 16 x 3
opt_achieved food n
<chr> <chr> <int>
1 No calcium 15
2 No fiber 15
3 No fruits 15
4 No legumes 15
5 No milk 15
6 No nuts and seeds 15
7 No polyunsaturated fatty acids 15
8 No processed meat 15
9 No red meat 15
10 No seafood omega-3 fatty acids 15
11 No sodium 8
12 No sugar-sweetened beverages 15
13 No vegetables 15
14 No whole grains 15
15 Yes sodium 7
16 Yes trans fatty acids 15all_age_diet_and_guidelines%>%
filter(sex == "Female",
location_name == "United States") %>%
count(opt_achieved, food)# A tibble: 16 x 3
opt_achieved food n
<chr> <chr> <int>
1 No calcium 15
2 No fiber 15
3 No fruits 15
4 No legumes 15
5 No milk 15
6 No nuts and seeds 15
7 No polyunsaturated fatty acids 15
8 No processed meat 15
9 No red meat 15
10 No seafood omega-3 fatty acids 15
11 No sodium 5
12 No sugar-sweetened beverages 15
13 No vegetables 15
14 No whole grains 15
15 Yes sodium 10
16 Yes trans fatty acids 15all_age_diet_and_guidelines %>%
filter(location_name == "United States") %>%
ggplot() +
geom_bar( postion = "dodge",
aes(x = food,
fill = opt_achieved)) +
theme(axis.text.x = element_text(angle = 70,
hjust = 1))
count(all_age_diet_and_guidelines %>%
filter(location_name == "United States"),
food,
opt_achieved) %>%
ggplot(aes(x = food,
fill = opt_achieved,
y = n )) +
geom_bar(position = "dodge",
stat = "identity") +
theme(axis.text.x = element_text(angle = 70,
hjust = 1))
all_age_diet_and_guidelines %>%
filter(location_name == "United States",
direction == "high") %>%
ggplot(aes(y = Relative_Percent,
x = food,
fill = age_group_name)) +
facet_wrap(~food, scales = "free") +
geom_boxplot() +
geom_point(position = position_jitterdodge(),
alpha = 3/10)
Let’s add gender to this. We will aslo use the viridis package to change the colors.
pd <- position_dodge(0.5)
all_age_diet_and_guidelines %>%
filter(location_name == "United States",
direction == "high") %>%
ggplot(aes(y = Relative_Percent,
x = sex)) +
facet_wrap_paginate(facets = .~food,
nrow = 3,
scales = "free",
strip.position = "right") +
geom_point(position = pd,
aes(fill = age_group_name),
alpha = 6/10,
col = "black",
#only shape 21-24 allow for fill and outline colors
shape = 23,
size = 3) +
#change the colors from rainbow to purple/green/yellow
scale_fill_viridis(discrete = TRUE) +
expand_limits(y=100) +
theme_linedraw()
Now let’s select only the foods that the optimum intake was not achieved:
pd <- position_dodge(0.5)
all_age_diet_and_guidelines %>%
filter(location_name == "United States",
direction == "high",
food %in% c(pull(filter(
all_age_diet_and_guidelines,
opt_achieved == "No"),food))) %>%
ggplot(aes(y = Relative_Percent,
x = sex)) +
facet_wrap_paginate(facets = .~food,
ncol = 2,
scales = "free",
strip.position = "right") +
geom_point(position = pd,
aes(fill = age_group_name),
alpha = 6/10,
col = "black",
#only shape 21-24 allow for fill and outline colors
shape = 23,
size = 3) +
#change the colors from rainbow to purple/green/yellow
scale_fill_viridis(discrete = TRUE) +
expand_limits(y=100) +
theme_linedraw()Now let’s take a look at the foods that were under-consumed:
pd <- position_dodge(0.4)
all_age_diet_and_guidelines %>%
filter(location_name == "United States",
direction == "low",
opt_achieved == "No") %>%
ggplot(aes(y = Relative_Percent,
x = sex)) +
facet_wrap_paginate(facets = .~food,
ncol = 3,
strip.position = "right") +
geom_point(position = pd,
aes(fill = age_group_name),
alpha = 6/10,
col = "black",
#only shape 21-24 allow for fill and outline colors
shape = 23, size = 3) +
#this changes the colors from rainbow to pupule/yellow/green
scale_fill_viridis(discrete = TRUE) +
ylim(c(0,100)) +
theme_linedraw()
pd <- position_dodge(0.5)
all_age_diet_and_guidelines %>%
filter(location_name == "United States",
direction == "low",
opt_achieved == "No") %>%
ggplot(aes(y = Relative_Percent,
x = food)) +
geom_point(position = pd,
aes(fill = age_group_name),
alpha = 6/10,
col = "black",
#only shape 21-24 allow for fill and outline colors
shape = 23, size = 3) +
#this changes the colors from rainbow to pupule/yellow/green
scale_fill_viridis(discrete = TRUE) +
ylim(c(0,100)) +
theme_linedraw() +
theme(axis.text.x = element_text(angle = 70,
hjust = 1))
pd <- position_dodge(0.5)
all_age_diet_and_guidelines %>%
filter(location_name == "United States",
direction == "low",
opt_achieved == "No") %>%
ggplot(aes(y = Relative_Percent,
x = food)) +
facet_wrap(~ sex, nrow = 2) +
geom_point(position = pd,
aes(fill = age_group_name),
alpha = 6/10,
col = "black",
#only shape 21-24 allow for fill and outline colors
shape = 23,
size = 3) +
#this changes the colors from rainbow to pupule/yellow/green
scale_fill_viridis(discrete = TRUE) +
ylim(c(0,100)) +
theme_linedraw() +
theme(axis.text.x = element_text(angle = 70,
hjust = 1))
Interesting, it looks like like both the younger adults and the elderly are generally doing worse than middle aged individuals in getting adequate intakes of these dietary factors.
We could also display the data differently with the x axis as age_group_name and the color as sex:
all_age_diet_and_guidelines %>%
filter(location_name == "United States",
direction == "low",
opt_achieved == "No") %>%
ggplot(aes(y = Relative_Percent ,
x = age_group_name,
fill = sex)) +
geom_point( data = all_age_diet_and_guidelines %>%
filter(location_name == "United States",
direction == "low",
opt_achieved == "No"),
alpha = 3/10,
size = 3,
aes(y = Relative_Percent ,
x = age_group_name,
fill = sex),
col = "black",
shape = 23) +
ylim(c(0,100)) +
facet_wrap_paginate(facets = .~food,
ncol = 2,
scales = "free") +
theme_linedraw() +
scale_fill_viridis(discrete = TRUE) +
theme(axis.text.x = element_text(angle = 70,
hjust = 1))
Now let’s focus on the really problematic foods:
#get list of foods with issues
trouble_foods <- all_age_diet_and_guidelines %>%
filter(location_name == "United States",
opt_achieved == "No",
Relative_Percent > 100 | Relative_Percent <= 25) %>%
select(food) %>%
distinct()
all_age_diet_and_guidelines %>%
filter(location_name == "United States",
food %in% pull(trouble_foods, food)) %>%
ggplot(aes(y = Relative_Percent ,
x = age_group_name,
fill = sex)) +
geom_point(alpha = 3/10,
size = 3,
aes(y = Relative_Percent ,
x = age_group_name,
fill = sex),
col = "black",
shape = 23) +
expand_limits(y=0) +
facet_wrap_paginate(facets = .~food,
nrow = 3,
scales = "free") +
scale_fill_viridis(discrete = TRUE) +
theme_linedraw() +
theme(axis.text.x = element_text(angle = 70,
hjust = 1))
We would like to get a general sense of how consumption of these dietary factors differs around the world and we would like to know how the US compares to other countries.
Before we do this let’s change the labels of the foods by adding new line breaks so that they will fit more easily.
diet_and_guidelines[["food"]] <- str_replace_all(
diet_and_guidelines[["food"]],
"sugar-sweetened beverages",
"sugar-sweetened\nbeverages")
diet_and_guidelines[["food"]] <- str_replace_all(
diet_and_guidelines[["food"]],
"seafood omega-3 fatty acids",
"seafood omega-3\nfatty acids")
diet_and_guidelines[["food"]] <- str_replace_all(
diet_and_guidelines[["food"]],
"polyunsaturated fatty acids",
"polyunsaturated\nfatty acids")
diet_and_guidelines %>%
select(food) %>%
distinct()# A tibble: 15 x 1
food
<chr>
1 "fruits"
2 "vegetables"
3 "whole grains"
4 "nuts and seeds"
5 "milk"
6 "red meat"
7 "processed meat"
8 "sugar-sweetened\nbeverages"
9 "fiber"
10 "seafood omega-3\nfatty acids"
11 "polyunsaturated\nfatty acids"
12 "trans fatty acids"
13 "sodium"
14 "calcium"
15 "legumes" all_age_diet_and_guidelines[["food"]] <- str_replace_all(
all_age_diet_and_guidelines[["food"]],
"sugar-sweetened beverages",
"sugar-sweetened\nbeverages")
all_age_diet_and_guidelines[["food"]] <- str_replace_all(
all_age_diet_and_guidelines[["food"]],
"seafood omega-3 fatty acids",
"seafood omega-3\nfatty acids")
all_age_diet_and_guidelines[["food"]] <- str_replace_all(
all_age_diet_and_guidelines[["food"]],
"polyunsaturated fatty acids",
"polyunsaturated\nfatty acids")
all_age_diet_and_guidelines %>%
select(food) %>%
distinct()# A tibble: 15 x 1
food
<chr>
1 "fruits"
2 "vegetables"
3 "whole grains"
4 "nuts and seeds"
5 "milk"
6 "red meat"
7 "processed meat"
8 "sugar-sweetened\nbeverages"
9 "fiber"
10 "seafood omega-3\nfatty acids"
11 "polyunsaturated\nfatty acids"
12 "trans fatty acids"
13 "sodium"
14 "calcium"
15 "legumes" To choose the colors for our plot we can use the show_col() function of the scales package (which is installed with ggplot2 package) to preview color options from the viridis palette of the viridis package.
We will use the position_jitterdodge() in our position argument of geom_point() to indicate how the points should be grouped by sex.
# first filter the values
Under <- diet_and_guidelines %>%
filter(direction == "low") %>%
filter(Relative_Percent < 110) %>%
ggplot(aes(y = Relative_Percent,
x = food,
color = sex)) +
# adds grey points for each country
geom_point(position = position_jitterdodge(),
color = "dark grey",
alpha = 7/10,
size = 7,
# this specifies how to separate the points
aes(fill = sex)) +
# add boxplots
geom_boxplot(outlier.shape = NA,
color = "black" ,
lwd = 2.5,
aes(fill = sex)) +
# this allows us to use specific colors
scale_fill_manual(values =
c("#481567FF",
"#1F968BFF")) +
# adds line for optimal amount
geom_hline(yintercept = 100,
linetype = "dashed",
color = "red",
size = 3) +
# manually changes y axis breaks
scale_y_continuous(breaks=c(0, 25, 50, 75, 100),
labels =c(0, 25, 50, 75,
"100% \n optimal amount"))
# creates a larger black point for the US data
Under <- Under +
geom_point(data = diet_and_guidelines %>%
filter(direction == "low",
location_name == "United States"),
position = position_jitterdodge(
jitter.width = 0.01,
dodge.width = 0.7),
color = "black",
size = 11,
aes(fill = sex,
shape = location_name))
# creates a smaller yellow point on top for the US data
Under <- Under +
geom_point(data = diet_and_guidelines %>%
filter(direction == "low",
location_name == "United States"),
position = position_jitterdodge(
jitter.width = 0.01,
dodge.width = 0.7),
color = "#FFDF00",
size = 7,
aes(fill = sex,
shape = location_name)) +
# make the plot look nice
theme_linedraw() +
theme(plot.title = element_text(size = 40,
hjust = 0.5,
face = "bold"),
axis.text.x = element_text(angle = 70,
hjust = 1,
size = 32),
# this is useful for removing the legend
legend.position = "none",
axis.text.y = element_text(size = 35),
axis.title.y = element_text(size = 35),
axis.title.x = element_text(size = 25),
panel.background = element_rect(colour = "black",
size = 1.5)) +
labs(title = "\n Global consumption of foods associated with\nhealth risk when under-consumed",
x = "",
y = "Percent consumption relative \n to guidelines")
Under
We will use the facet_zoom() function of the ggforce package to create a plot with a zoomed in portion.
Over <- diet_and_guidelines %>%
filter(direction == "high") %>%
ggplot(aes(y = Relative_Percent,
x = food)) +
# this adds points for each country
geom_point(position = position_jitterdodge(),
color = "dark grey",
alpha = 7/10,
# this specifies how to separate the points for male and female
aes(fill = sex),
size = 7) +
# this adds boxplots
geom_boxplot(outlier.shape = NA,
color = "black" ,
lwd = 2.5,
#key_glyph = draw_key_rect),
aes(fill = sex)) +
# this manually changes boxplot colors
scale_fill_manual(values =
c("#481567FF",
"#1F968BFF")) +
# this adds optimal red line
geom_hline(yintercept = 100,
linetype = "dashed",
color = "red",
size = 3) +
# this changes y axis breaks
scale_y_continuous(breaks = c(100, 500, 1000, 2000),
labels = c("100% thresh.", 500, 1000, 2000)) +
# this zooms in to part of the plot
ggforce::facet_zoom(ylim = c(0, 1300)) +
# this changes the legend direction
guides(guide_legend(direction = "horizontal",
label.vjust = .5))
# creates a larger black point for the US data
Over <- Over +
geom_point(data = diet_and_guidelines %>%
filter(direction == "high",
location_name == "United States"),
position = position_jitterdodge(
jitter.width = 0.01,
dodge.width = 0.7),
color = "black",
size = 11,
aes(fill = sex,
shape = location_name))
# creates a smaller yellow point on top for the US data
Over <- Over +
geom_point(data = diet_and_guidelines %>%
filter(direction == "high",
location_name == "United States"),
position = position_jitterdodge(
jitter.width = 0.01,
dodge.width = 0.7),
color = "#FFDF00",
size = 7,
aes(fill = sex,
shape = location_name)) +
scale_color_discrete(breaks = "United States") +
# this makes the plot look nice
theme_linedraw() +
theme(plot.title = element_text(size = 40,
hjust = 0.5,
face = "bold"),
axis.text.x = element_text(angle = 60,
hjust = 1,
size = 32),
legend.title = element_blank(),
legend.position = "bottom",
# this is for changing the zoom triangle color
strip.background = element_rect(fill = "grey86",
colour = "grey86"),
axis.text.y = element_text(size = 35),
axis.title.y = element_text(size = 35),
axis.title.x = element_text(size = 25),
legend.text = element_text(size = 30,
vjust = .01),
# this is for changing the legend symbol size
legend.key.height = unit(2, "cm"),
legend.key.width = unit(2, "cm"),
# this is for changing the figure outline
panel.background = element_rect(colour = "black",
size = 1.5)) +
expand_limits(y = -10) +
labs(title = " Global consumption of foods associated with\n health risk when over-consumed",
x = "",
y = "Percent consumption relative \n to guidelines")
Over
Let’s put the plots together:
To make this plot we will use the facet_wrap_paginate() function of the ggforce package which allows you to specify the number of columns or rows for the facets.
Over_age <- all_age_diet_and_guidelines %>%
filter(direction == "high") %>%
filter(Relative_Percent < 4000) %>%
ggplot(aes(y = Relative_Percent,
x = age_group_name,
fill = age_group_name)) +
geom_boxplot(outlier.shape = NA,
color = "black" ,
aes(fill = age_group_name)) +
geom_hline(yintercept = 100,
linetype = "dashed",
color = "red",
size = 2) +
theme_linedraw() +
theme(plot.title = element_text(size = 30,
hjust = 0.5,
face = "bold"),
axis.text.x = element_blank(),
legend.title = element_blank(),
legend.position = "bottom",
axis.text.y = element_text(size = 28),
axis.title.y = element_text(size = 30),
axis.title.x = element_text(size = 30),
strip.text.x = element_text(size = 25,
face = "bold"),
legend.text = element_text(size = 25),
legend.key.height = unit(1.2, "cm"),
legend.key.width = unit(1.2, "cm"),
panel.background = element_rect(colour = "black",
size = 1.5)) +
labs(title = " Global consumption across age\n of foods associated\n with health risk when over-consumed",
x = "Age",
y = "\nPercent consumption relative \n to guidelines")+
ggforce::facet_wrap_paginate(~food,
ncol = 2,
scales = "free") +
scale_fill_viridis(discrete = TRUE)
Over_age
hmmm these look a bit funny because the outliers in the boxplots for the foods besides sugar-sweetened beverages are making the range much larger than the boxplot themselves. We can calculate the values for the boxplots our selves to deal with this:
calc_stat <- function(x) {
coef <- 1.5
n <- sum(!is.na(x))
# calculate quantiles
stats <- quantile(x, probs = c(0.1, 0.25, 0.5, 0.75, 0.9))
names(stats) <- c("ymin", "lower", "middle", "upper", "ymax")
return(stats)
}Then we can use it in our plot code:
This is thanks to this: https://stackoverflow.com/questions/25124895/no-outliers-in-ggplot-boxplot-with-facet-wrap
#map_df(all_age_diet_and_guidelines %>% select(Relative_Percent), calc_stat)
Over_age <- all_age_diet_and_guidelines %>%
filter(direction == "high") %>%
ggplot(aes(y = Relative_Percent,
x = age_group_name,
fill = age_group_name)) +
# here we will replace geom_boxplot() yet still create boxplots
stat_summary(fun.data = calc_stat,
geom = "boxplot",
outlier.shape = NA,
color = "black" ,
lwd = 1.1,
aes(fill = age_group_name)) +
geom_hline(yintercept = 100,
linetype = "dashed",
color = "red",
size = 2) +
theme_linedraw() +
theme(axis.text.x = element_text(angle = 60,
hjust = 1),
legend.title = element_blank()) +
theme_linedraw() +
theme(plot.title = element_text(size = 30,
hjust = 0.5,
face = "bold"),
axis.text.x = element_blank(),
legend.title = element_blank(),
legend.position = "bottom",
axis.text.y = element_text(size = 28),
axis.title.y = element_text(size = 30),
axis.title.x = element_text(size = 30),
strip.text.x = element_text(size = 25,
face = "bold"),
legend.text = element_text(size = 25),
legend.key.height = unit(1.2, "cm"),
legend.key.width = unit(1.2, "cm"),
panel.background = element_rect(colour = "black",
size = 1.5)) +
labs(title = " Global consumption across age\n of foods associated\n with health risk when over-consumed",
x = "Age",
y = "\nPercent consumption relative \n to guidelines") +
guides(fill = guide_legend(nrow = 5,
byrow = TRUE)) +
facet_wrap_paginate(~food, ncol = 2,
scales = "free") +
scale_fill_viridis(discrete = TRUE)
Over_age
Under_age <- all_age_diet_and_guidelines %>%
filter(direction == "low") %>%
ggplot(aes(y = Relative_Percent,
x = age_group_name,
fill = age_group_name)) +
# here we will replace geom_boxplot()
stat_summary(fun.data = calc_stat,
geom = "boxplot",
outlier.shape = NA,
color = "black",
lwd = 1.1,
aes(fill = age_group_name)) +
geom_hline(yintercept = 100,
linetype = "dashed",
color = "red",
size = 2) +
theme_linedraw() +
theme(axis.text.x = element_text(angle = 60,
hjust = 1),
legend.title = element_blank(),
legend.position = "bottom") +
facet_wrap_paginate(~food, nrow = 5, scales = "free") +
theme_linedraw() +
theme(plot.title = element_text(size = 30,
hjust = 0.5,
face = "bold"),
axis.text.x = element_blank(),
legend.title = element_blank(),
legend.position = "none",
axis.text.y = element_text(size = 28),
axis.title.y = element_text(size = 30),
axis.title.x = element_text(size = 25),
strip.text.x = element_text(size = 25,
face = "bold"),
panel.background = element_rect(colour = "black",
size = 1.5),
axis.ticks.x = element_blank()) +
labs(title = "\n Global consumption across age\n of foods associated\n with health risk when under-consumed",
x = "",
y = "\nPercent consumption relative \n to guidelines") +
scale_fill_viridis(discrete = TRUE)
Under_age
Now let’s put all the plots together:
We have evaluated average consumption estimates of 15 dietary factors with probably non-communicable disease (NCD) risk from 195 different countries around the world. To do so we imported data from a PDF using the pdftools package, as well as data from two CSV files using readr. We used tidyverse packages such as dplyr, stringr, and tidy to clean and join the data from the PDF with the CSV files.
We learned that regression is a powerful and flexible statistical tool that simplifies or estimates the relationships between variables using a mathematical model. We learned about the utility of regression techniques to compare groups, look for associations between variables, and predict outcomes based on multiple predictor or explanatory variables. We then compared this to other popular tests like the \(t\)-test and the ANOVA. We learned that these tests are actually equivalent to specialized types of regressions.
Our statistical analysis focused on evaluating differences in the consumption of red meat around the world between females and males and across different age groups. First we looked at the assumptions of \(t\)-tests and regressions, and determined that the rate of red meat consumption relative to the optimal guideline-suggested amount was right skewed. We learned that we could transform the data by taking the log of these values to achieve more normally distributed data. To compare males and females we used a \(t\)-test and learned that a \(t\)-test is a specialized form of a linear regression. To compare the 15 different age groups we used an ANOVA and learned that ANOVA is also a specialized form of linear regression. We examined how we obtained the same results using either statistical test. This was also the case if we looked at the effect of gender and controlled for the paired country structure in the data by either including location_name in the model as another term or by using a mixed effects model to control for this structure as a random effect but not specifically test for the influence of location_name on red meat consumption estimates. We learned that fixed effects are those that we wish to evaluate, while random effects are those that may influence the relationships of our variables of interest but that we do not wish to actively evaluate. Using these tests and models, we determined that males consume more red meat than females on average around the world.
Our ANOVA analysis of age determined that indeed there is at least one age group that consumed a significantly different amount of red meat compared to the other age groups, and this was still the case when we controlled for location_name. However, we learned that the ANOVA does not provide information about which age groups are different. We learned how the regression could provide some quantification of the effect of specific age groups relative to the reference age group. Furthermore, our data visualizations allowed us to determine that in general red meat consumption appears to be higher in the younger age groups relative to the older age groups.
Finally, we also looked at differences in red meat consumption between the different countries and saw in our ANOVA analysis and our regression analysis that there were significant differences. We were able to use a regression that included sex, age_group_name, and location_name to evaluate the influence of each of the three demographic factors on consumption while controlling or accounting for the other two. Our results demonstrated that all three influenced or were associated with red meat consumption.
In preforming our statistical analyses we learned about the assumptions of the \(t\)-test, regression, and the ANOVA. We also learned about important methods to tests these assumptions.
We can evaluate if our data is normally distributed by plotting the distribution and by creating [Q-Q plots](Q-Q Plots to compare the distribution of our data to the theoretical normal distribution.
If our data is not normally distributed, we can consider these options:
See this case study for more information on \(t\)-test assumptions.
We learned that we can test if 2 groups have equal variance using:
1) the F test with var.test()
2) Mood’s test using mood.test() (use if the data are not normally distributed)
L (linear) - There is a linear relationship between the variables.
I (independent) - The samples are independent from one another.
N (normal) - The residuals are normally distributed.
E (equal variances) - The variance of the residuals of the groups is similar.
It is also important that predictor variables are not correlated with one another.
We learned about three tests for examining the equality of variance of 3 or more groups: 1) Bartlett’s test (works well if the data appears to be normally distributed) 2) Fligner-Killeen test is nonparametric and does not assume normality 3) Levene’s test, is more robust to violations of normality than the Bartlett’s test, but not as robust as the Fligner-Killeen test
Using the ggplot2 package we were able to visualize trends in the data and to compare consumption of these dietary factors in the US with that of the other countries. We see that the populations in many countries are over-consuming foods that are associated with health risk when over-consumed. In particular processed meat and sugar-sweetened beverages appear to be the most over consumed. Importantly both of these appear to be consumed at higher quantities by males and younger adults. People in the US appear to consume fewer sugar-sweetened beverages than other countries, however, people are still over-consuming. Processed meat however appears to be especially bad in the US. In terms of food that need to be consumed in adequate amounts to overcome health risk, nearly all countries for all factors are not reaching guideline levels. However, there are some countries consuming more than adequate amounts of legumes, vegetables, fruits and fiber. People in the US appear to eat more milk products and consume more omega-3 fatty acids and calcium rich foods than other countries. All countries including the US consume very low levels of polyunsaturated fatty acids. These polyunsaturated fatty acids are abundant in seeds, nuts and avocados, as well as fish. Likely the low level of consumption of nuts and seeds contributes to these low polyunsaturated fatty acid estimates. The supplementary table included in the article suggests that poor consumption of polyunsaturated fatty acids is associated with ischemic heart disease. The article takes this data further to evaluate the association of consumption levels of these foods with mortality.
Analyses like the one in our case study are important for defining which groups could benefit the most from interventions, education, and policy changes when attempting to mitigate public health challenges. You can see in the article however that many additional considerations would be involved to perform a more thorough analysis to adequately understand the data enough to recommend policy changes.
Students can evaluate consumption estimates of another dietary factor besides red meat.
Terms and concepts covered:
Tidyverse
RStudio cheatsheets
Interpunct
Regular expressions
Inference
Regression
Different types of regression
Ordinary least squares method
Residual
\(t\)-tests
ANOVA
\(t\)-tests and ANOVA are equivalent to regression also see here and here about how many commonly known statistical tests are specialized forms of regression
Normal Distribution
Q-Q plot
Guide to residual diagnostic plots and Examples
Residual vs fitted plot
Scale-location plot
Homoscedasticity
Heteroscedasticity
Interpreting lm() output
Coefficients
Linear mixed effects regression
Satterthwaite formula
Mood’s Two-Sample Scale Test
Standard deviation
Homogeneity of Variances assumption
polyunsaturated fatty acids
Tests of Homogeneity of Variance for 3 or more groups:
Bartlett’s test
Fligner-Killeen
Levene’s test
Other helpful links:
Long and Wide Data Formats
Distributions Skewed Distributions Bimodal Distribution ggplot2
Shapiro-Wilk Test
Paired Data
Welch’s \(t\)-test
Parametric and Nonparametric Methods
Variance
Balanced Study Design
Independent Observations
Transformation
Permutation/Resampling Methods
Central Limit Theorem Wilcoxon Signed Rank Test
Wilcoxon Rank Sum Test
Two-sample Kolmogorov-Smirnov Test
Type 1 Error
p-value
Multiple Testing
Bonferroni Method of Multiple Testing Correction
Packages used in this case study:
| Package | Use |
|---|---|
| here | to easily load and save data |
| readr | to import the CSV file data |
| dplyr | to arrange/filter/select/compare specific subsets of the data |
| skimr | to get an overview of data |
| summarytools | to get an overview of data in a different style |
| pdftools | to read a PDF into R |
| stringr | to manipulate the text within the PDF of the data |
| magrittr | to use the %<>% piping operator |
| purrr | to perform functions on all columns of a tibble |
| tibble | to create data objects that we can manipulate with dplyr/stringr/tidyr/purrr |
| tidyr | to separate data within a column into multiple columns |
| ggplot2 | to make visualizations with multiple layers |
| ggpubr | to easily add regression line equations to plots |
| forcats | to change details about factors (categorical variables) |
| lmerTest | to perform linear mixed model testing |
| car | to perform Levene’s Test of Homogeneity of Variances |
| ggiraph | to make plots interactive |
| ggforce | to modify facets in plots |
| viridis | to plot in color palette |
| cowplot | to allow plots to be combined |